CICTP 2020: Transportation Evolution Impacting Future Mobility

Traffic congestion is a common traffic anomaly in many large-scale cities. The research on video-based traffic congestion evaluation methods is the main trend of traffic congestion detection, but its discrimination on the level of congestion remains to be studied. In this paper, we introduce a traffic congestion estimation method based on traffic surveillance video feature extradition named TCNet (traffic congestion net). Taking the density and speed of traffic flow as the congestion estimation standard, the vehicle detection and speed estimation as the technical core, TCNet can provide a more accurate description of the congestion level. TCNet uses improved YOLOv3 module to detect the image to get the number of vehicles on the road, using TBBFA (traffic bounding box filtering algorithm) to remove the redundant and error bounding boxes, thereby getting the accurate traffic flow density. Finally, we use the TCMA (timing-based center matching algorithm) to calculate the driving speed measured by pix/sec of each detected vehicles. With the above-calculated parameters, we can finally calculate the level of congestion. For practical application, TCNet’s detection time is optimized to achieve the effect of real-time surveillance detection.

With the continuous development of autonomous driving technology, the test of self-driving cars has become an important component. This paper proposes a pedestrian accident scenario construction method based on traffic accident data, selects 390 pedestrian accidents that meet the requirements from the traffic accident data, and selects the appropriate data composition by analyzing the definition, structure, function, and test scenario of the AEB system. The analytic hierarchy process is used to sort the selected parameters, and the degree of influence of each parameter on the scenario construction is obtained, thereby rapidly generating an ideal testing scenarios. Finally, based on a real accident data, a test scenario is created to verify the feasibility of the scenario construction method.

How to effectively identify traffic bottlenecks on the freeway and accordingly implement targeted countermeasures remains a critical issue for traffic management and control. With the increasing development of GPS-embedded smartphone navigation, vehicle trajectory data collected in a crowdsourcing way provides a means of constructing spatiotemporal diagrams to support the decision-making process for traffic authorities. To this end, this study presents a comparative case study by comparing two technical methods, i.e., wavelet transform and image processing, which enable to facilitate the identification of recurrent traffic bottlenecks on freeways using vehicle trajectory data. The data utilized were 45-day probe vehicle data collected on urban expressways of Beijing during January and February 2015. The validation results by referring to field bottlenecks show that the image processing method outperforms wavelet transform in terms of estimation accuracy.

As the main body of urban traffic, commuting travel can help to determine the state of urban road traffic. With the quick development of big data technology, how to identify the urban road traffic state quickly, accurately, and at low cost is the only way to realize the intelligent transportation system. Based on the identification of commuting travel by 4G mobile phone signaling data, convolutional neural network (CNN) which can solve urban network problems was applied to the road traffic state identification problem for the first time. Taking the traveling OD and commuting OD as model input, the state mode was extracted from the historical data to achieve accurate capture of the relationship between traffic demand and traffic operation status in the designed CNN model. The results show that using mobile phone data to extract traveling OD and commuting OD as the data input of the CNN model can better identify the urban road traffic state, and the model has an accuracy of 88.4%.

Traffic mixed with manual vehicles (MVs) and automated vehicles (AVs) will eventually occur. While quantitative analysis of variation trend of penetration rate affecting traffic efficiency and safety is insufficient. This paper studies the influence of typical factors on the mixed traffic flow by extensive simulation experiments. In particular, with VISSIM, the trend of traffic efficiency with the change of AVs’ penetration rate is discussed. An intelligent driver model (IDM) and classic psycho-physiological following model Wiedemann99 are introduced to describe the characteristics of AVs and MVs, respectively. The influence on traffic safety is studied with different time headways, which is a parameter in IDM model parameters. Simulation results illustrate that under the condition of low penetration rate of AVs, the traffic efficiency will increase. When AVs’ penetration rate reaches the peak, the traffic efficiency can increase by 40%. The introduction of AVs will improve the traffic efficiency of mixed traffic flow.

This paper develops a KNN Model weighted by the importance of characteristic variables of the Random Forest, to predict the travel time between two adjacent signalized intersections. The importance of each characteristic variable is calculated by the Gini coefficient evaluation index based on the Random Forest model, and is weighted into the KNN model to predict the travel time. In the case study, the density and ifmpact of traffic lights are selected as characteristic variables due to their close relationship with travel time. The travel times are clustered by the DBSCAN algorithm to distinguish the number of stops affected by the traffic lights. Experimental results demonstrate that the proposed model provides an effective approach for urban travel time prediction and outperforms the considered competing methods. Combined with the Dijkstra’s Algorithm, the proposed model is applied to the road network to find the shortest travel time path.

To ensure emergency vehicles arrive at their destination as soon as possible, this paper presents a method to plan an optimal path for emergency vehicles based on urban traffic status analysis. Firstly, the saturation of each lane group at the intersection in the road network is analyzed. Then, the lane group saturation prediction method based on the long short-term memory (LSTM) network model is proposed. According to the lane group saturation prediction and the travel time estimation, the optimal path for emergency vehicles is selected. Finally, the test area is selected to verify the method. The example proves that this method can effectively plan the path with the shortest travel time for emergency vehicles from all possible paths during peak hours. It is reliable to plan optimal path for emergency vehicles.

Short-term traffic flow prediction based on multi-model combination under Stacking framework is proposed in this paper, associated with frontier theory research of artificial intelligence. Firstly, the mechanism of Stacking ensemble learning is introduced. The XGBoost algorithm constructed by tree model and the deep learning algorithm represented by LSTM are presented. The Stacking based traffic forecasting model embedded various machine learning algorithms is proposed to utilize their diversified strength. In this stacking framework, the XGBoost algorithm, the LSTM algorithm, the support vector regression and k-Nearest Neighbor are chosen as base-learners, and the XGBoost algorithm is chosen as the meta-learner. Finally, the effectiveness of the algorithm is verified by actual traffic flow data which showed that the forecasting results are more accurate when each of base-learner has lower correlation coefficient in stacking. The results indicated the stacking ensemble learning based on multi-model has better prediction performance compared with the traditional single model.

According to the characteristic of real-time information interaction under the auto-driving mode, comprehensively considering the uncertainty of network traffic demand, and the balance of traffic flow at the bottleneck of intersections, a bi-level optimization model for dynamic traffic assignment and signal control under auto-driving mode is proposed. In the lower layer, each vehicle travels along the shortest path determined by the OD data obtained from the autopilot system, which reflects the user’s optimization, then the traffic demand of each direction at intersections in a certain interval can be predicted. On the premise of meeting the traffic demand, the control mode with minimum delay is selected. The objective of the upper layer is to minimize the average delay of vehicles on a road. The traffic demand of the upper layer is determined by the lower layer, and the dynamic traffic distribution of vehicles in the lower layer is affected by the intersection control mode determined by the upper layer. Combining frank-wolf algorithm and enumeration method, an iterative algorithm is proposed. The numerical simulation experiments verify the correctness and feasibility of the model and the algorithm. The results show that the model achieves the flexible choice of cross signal control mode and the optimal allocation of traffic flow, which reduces the delay of vehicles significantly, and it improves the efficiency of road and controls the occurrence of intersection supersaturation effectively.

The last decade has witnessed the prevalence of sensor and GPS technologies that produce an enormous volume of trajectory data describing city dynamics and human behaviors. Detecting trajectory anomalies is undoubtedly one of the most important tasks in trajectory data management since it serves as the foundation of many advanced analyses such as suspicious behavior identification during important events. Tremendous efforts have been spent on this topic, but previous studies have required time-consuming preprocessing or are susceptible to the model parameters. In this paper, we first measure the similarity between trajectories through dynamic time warping. We then use a robust density based clustering method of ordering points to identify the clustering structure, to distinguish the anomalous cases. We used a real-world taxi trajectory dataset in Beijing to validate the effectiveness of our approach.

The point merge system (PMS) aims to solve the sequencing and scheduling problem for inbound aircraft traffic, which is currently handled by controllers. This study proposes a modeling method and a heuristic algorithm for handling arrival sequencing and the scheduling problem by taking advantage of the unique structure of the PMS. First, we introduced the structure and operational mode of the PMS. Second, the PMS based arrival sequencing and scheduling model was constructed considering the operational constraints (like safety separation and time windows) and objective functions (maximize capacity enhancement). Third, a heuristic algorithm (adaptive genetic algorithm based on elite strategy) was proposed to solve such a problem. Finally, the inbound traffic of Changsha Huanghua International Airport was used to validate the algorithm.

Changing lanes is one of the most common driving behaviors in traffic flow, making it a key factor in causes of traffic accidents and congestion. Previous studies have primarily used a single data type collected from internet of things sensors, and did not take every aspect into consideration to fully evaluate driver behavior. In this work, we propose a machine learning-based approach for lane-changing behavior evaluation using data collected from multiple sensors. The lane change behavior data was collected by the motion sensors embedded in smartphones and multiple cameras installed inside the vehicle. A list of features was extracted from the collected data and a decision tree was constructed to evaluate lane-changing behaviors. The collected lane-changing behavior data was split into a training and a testing data set to build and validate the decision tree. The validation results show that the proposed model can accurately evaluate different lane-changing behaviors. This study also provides theoretical support for monitoring and early warning of lane-changing behavior for connected vehicles.

This paper is concerned with the problem of designing a decentralized controller for connected vehicle platoon driving on curved roads. The controller is proposed by considering the states of the subject vehicle and its immediately preceding vehicle as well as the characteristics of the curved road based on the third-order dynamic model. In particular, the longitudinal and lateral controllers for vehicle platoon are developed, respectively. Then, the stability of the proposed longitudinal and lateral controllers is analyzed using the Hurwitz stability criterion. Furthermore, extensive simulations are conducted to verify the effectiveness of the proposed controllers with respect to speed and acceleration/deceleration profiles.

In recent years, the development of big data acquisition and storage, computer technology and communication technology has provided new momentum for ITS, while traffic speed prediction is a core link of ITS. In order to achieve large-scale traffic forecasting in urban road network and extract the time series feature and spatial feature of road network speed evolution, a spatiotemporal prediction method based on 3D convolution neural network is proposed in this paper, using gridded historical traffic data and corresponding road network traffic speed for training. Finally, in the empirical analysis stage, 3D CNN is evaluated and compared with the prediction results of 2D CNN, LSTM, and BPNN models on the whole, midweek and weekend. Experimental results show that the MAE, MAPE, and RMSE indices of the test set are at least 10% better than other models. It has a good performance in the actual road network traffic speed prediction.

With growth of urbanization, urban traffic becomes more and more congested. As an important node of traffic flow convergence and diversion, the intersection efficiency will affect the operation efficiency of the urban transportation system to a large extent. The existing intersection signal control mostly uses the empirical fixed signal timing. The empirical fixed signal timing seriously affects the traffic efficiency of the intersection. This paper starts with the signal control method of a single intersection in the city, and improves the operation efficiency of the intersection by optimizing the signal control method of the intersection. This paper proposes the intersection signal control method based on reinforcement learning, and adopts the Q-learning reward and punishment signal design method, implementing optimization of intersection signal control. Finally, the simulation experiment of the intersection is carried out by PTV-Vissim9.0 software to verify the feasibility of the theory.

In order to construct signalized intersections which are more in line with the actual traffic operation mechanism we propose a new traffic flow model for intersections which is a hybrid between the classical cellular automata and the other microscopic traffic models, using continuous cellular automata to analyze the influence of driver heterogeneity and split. This continuity allows us embed a multiple car following model to simulate different car-following behaviors. The experimental results show that our model is able to reproduce the typical traffic flow phenomena with a variety of effects due to the heterogeneity within traffic stream.

The prevention of foreign object debris in domestic small and medium-sized airports mainly depends on manual detection. To reduce the cost of detection of foreign object debris in airport flight areas, a method that relies on image detection to identify foreign invasive objects at the airport is proposed. To improve the reliability of the saliency detection, the classical ITTI model is improved. The D-S evidence fusion theory is used to fuse features according to the degree of support, and saliency detection is performed on the processed images. The simulation was performed in 500 samples. The experiment proves that the accuracy of the foreign object recognition in the image is over 92%, which is about 11% higher than the highest color feature detection in the single feature. The unrecognized condition and the detected noise dropped below 10%, which greatly increased the efficiency of foreign object recognition.

Accurate flight departure delay forecasting is essential for reliable travel scheduling in intelligent air transportation systems. A two-stage approach is proposed to classify flight departure delay in the future for airports. We first use a clustering algorithm to set the classification rule according to flight departure delay extracted from history information. In the second stage, several state-of-the-art ensemble learning models, which include random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), are adopted for the flight departure delay classification. The flight departure delay classification models are trained and validated on flight data collected from Beijing Capital International Airport (PEK). The results show that the LightGBM model performs the best among the four employed models for classifying the flight departure delay. The performance comparison of the models can provide valuable insights for researchers and practitioners.

The number of private cars has grown rapidly in recent years, making road congestion, environmental pollution, and energy consumption increasingly serious. Carpooling is considered to be an effective way to solve the problems. Several travelers with similar travel routes can save money and reduce fuel consumption by carpooling, which improves vehicle and road utilization. Due to the widespread use of smartphones and mobile application development platforms, carpooling has broad prospects for development. In the carpooling problem, whether there is a high temporal and spatial correlation between the trajectories is the key to determine whether or not to match. This paper analyzes the potential of carpooling, using trajectory data, simplifying the trajectory through the trajectory area gridization process, and proposes several carpool models, and uses this as a basis to analyze the trajectory data from time and space. The final solution model is a maximum weight non-bipartite graph match.

As most of the nodes in vehicular ad-hoc networks (VANET) are fast-moving vehicles, the mobility of the nodes makes the structure of the network topology become more complex, the distribution range of the nodes become more extensive, and the relative position of the nodes become more flexible and uncontrollable. All these uncertain factors affect the stability of communication between vehicular nodes and the accuracy of vehicle data. This paper analyzes and compares the performance of the routing algorithm in the opportunistic networks (OppNets) under different preset values, to determine an efficient routing algorithm which is more suitable for the complex and changeable environment of the current VANET, and improves the communication between nodes. Simulation results show that the performance of each routing algorithm is significantly different under different preset values. Among them, the spray and focus routing algorithm performs the best, which improves the transmission rate and reduces the delay.

Estimation of travel time is a key factor of traffic guidance and transportation management. However, it is impossible to collect the time of passing all roads during the data collection phase. Due to the sparseness of data, the prediction accuracy of existing methods is usually not satisfactory. In this paper, we consider introducing the concept of tensor to make full use of spatial-temporal traffic patterns. A model based on tensor decomposition for estimating the travel time from the citywide perspective is proposed. This model is comprised of three major components: travel time tensor construction, travel time tensor factorization, and unconstrained optimization. Different dates, time periods, and road segments are modeled as three dimensions of tensors. An example analysis of the monitoring data collected is provided. This paper verifies conditions in which missing data at different degrees, the accuracy of the model. Results show the model can achieve great prediction accuracy.

In this research, mobile phone data and road network topology data were used to study highway networks status, especially detecting critical roads. Spatial and temporal properties of the data from mobile phones to the road network was mapped. Then the user’s displacement over a period of time generated the OD matrix, community detection algorithm Infomap algorithm needs to divide the n nodes in the network into m clusters in order to minimize the expected description length of the random walk. This principle can detect critical links. One month of mobile phone data covering the road network formed by the two highways in Beijing was used. Experiments show the detection of road networks data with different weights will result in different types of critical link segments. The critical link overlap ratio obtained by the road network with a weight of 1 and a weight of flow is only around 60%.

In order to study the traffic efficiency of ramp intersections, the Monte Carlo simulation experiment was carried out by programming based on traffic flow theory and critical gap theory, and the relationship between the average delay and total conflict traffic volume was studied at a ramp intersection. According to the simulation results, the average delay of vehicles increased slowly first, and then sharply with the increase of the total conflict traffic volume. Moreover, when the main traffic volume (Qm) was greater than the minor traffic volume (Qc), the average delay, and total conflict traffic volume had higher correlation and fitting degree. Therefore, the relationship model between the two variables was established in the case of Qm ≥ Qc. Finally, according to the existing specification about average delay at different service levels for unsignalized intersection, the total conflict traffic volume to different service levels at ramp intersections was back-calculated based on the relationship model.

Speed prediction is essential for a wide range of traffic control measures, such as proactive control. Moreover, the estimation of individual vehicle speed enables more accurate traffic optimization and thus, is vital in the design of proactive traffic control method. Most of the previous studies investigated the speed prediction relying on macroscopic traffic data since it is difficult to obtain realtime microscopic traffic data. This paper proposes a novel methodology based on the kinematical characteristics of steady-state traffic flow for predicting the individual vehicle speed utilizing artificial intelligence, such as neural network and support vector machine. The microscopic and macroscopic traffic data sets used to train, test and validate were collected from a real world stretch of freeway. The evaluation of the proposed model indicates that the proposed model is contributed to predicting individual vehicle speed.

With significantly improved positioning accuracy and coverage, trajectory data collected from GPS equipped probe vehicles have great potential for traffic speed estimations. This paper presents an innovative algorithm for estimating the traffic speed on the urban road network based on GPS data. The authors firstly adopt the GIS digital map as the foundation of mapping the GPS trajectory data to the traffic network. Secondly, a novel synthetic algorithm for traffic speed reconstruction is proposed, which assign dynamic weights to GPS records according to their different velocities and take into account the spatial and temporal relevancy. The complex relevancy of speed across different segments and time periods was identified using an artificial neural network (ANN) model. At last, the proposed methodology was evaluated by comparing estimation speed with ground truth obtained from fixed detectors in Zhangzhou, China. The experiment results clarify that the proposed algorithm is effective and efficient in reconstructing the traffic state of urban road networks using GPS equipped probe vehicle data.

The efficiency evaluation of a signal control intersection is the basis of signal optimization. Green time utilization is one of the most important evaluation indicators. In current studies, the most common input is traffic volume data from fixed detectors, which might not be accurate or available. This paper proposes a method using trajectory data instead. Based on the stop-wave and dissipation-wave information, three indicators were proposed to comprehensively evaluate the utilization of green time. A field case study was used to show the effectiveness, and a sensitivity analysis was carried out to illustrate the applicability. Results showed that when the penetration rate reaches 10% with a 2 h database or when the total sampling time reaches four hours under the penetration rate of 5%, the mean absolute percent error value of the overall green time utilization rate will be less than 10% and the performance will be relatively stable.

With the continuous development and improvement of automatic-driving technology and connected-vehicles, various real-time information is available now. To improve the operational reliability and service quality of buses, a dynamic speed guidance strategy for autonomous buses is proposed in this paper. When it comes to the situation of autonomous buses passing through the intersection, a speed curve planning model based on the trigonometric curve is established with the aim to reduce the number of stopping. The effect of the dynamic speed guidance was analyzed through simulation. Results show that the proposed dynamic speed guidance for autonomous bus can effectively improve the operating efficiency of the bus. The dynamic speed guidance strategy proposed in this paper can improve the operational reliability of buses, thus improving the service level of buses.

Carpooling is considered an important solution to alleviate traffic congestion and reduce air pollution in cities. In this study, trajectory cleaning is performed on the data set, trajectory reconstruction is performed on adjacent trajectory breakpoints which larger than the entire sub-region, and time-lag trajectory data is eliminated, so that points of the trajectory are divided into sub-regions by coordinates, finally, the matching of the trajectory points is converted into the matching of the nodes of each sub-region. Based on the perfect match and the starting point match (O-D match), a trajectory adjustment similarity matching mode that does not change the total travel distance is explored, and all matching matches are screened by the space-time similarity recognition algorithm based on the longest common subsequence (LCS). The trajectory matching pairs are calculated using the blossom algorithm to achieve the maximum trajectory matching under the influence of different waiting times on the train.

The cooperative vehicle infrastructure system (CVIS) is used to acquire real-time information such as vehicle position, vehicle speed, queue length, and signal status by wireless communication, and sensor detection technology. In order to improve the traffic efficiency in the intersection, this paper proposes a speed guidance strategy by analyzing the driving behavior of autonomous vehicles in the intersections. In the cellular automata model, the vehicle location and speed can be obtained every second. In this paper, the NS (NaSch) model of cellular automaton model is improved according to the speed guidance strategy, and the speed guidance model (SG model) based on cellular automaton. The results show that the model can effectively increase the number of vehicles passing through the intersection.

Inclement weather conditions may have a significant influence on traffic state estimation and prediction. To achieve more accurate estimation and prediction, it is essential to consider meteorological influence in the traffic flow estimation. This study first introduces an image-based method to analyze relevant meteorological features, using meteorological images data from the China National Meteorological Administration. We extract the features of hourly rainfall, hourly temperature, hourly humidity, and daily wind speed data across the country. The relationship between variables in the traffic flow model in various weather conditions in Ningbo City is analyzed using of traffic survey station data. The results show that temperature, illumination, and precipitation have a greater impact on some parameters in the traffic flow model, such as flow rate and average speed. Therefore, the traffic flow model analysis incorporating weather impacts can be effectively used for traffic state estimation and prediction under rainy conditions in Ningbo City.

The sensitivity of the traffic detector or the influence of the external environment will cause different degrees of data missing or abnormal. In addition, the traffic parameters collected by different traffic detectors are different. Therefore, it is very important to identify key data sources for multi-source data fusion for different research purposes. This paper establishes a key data source evaluation system, and puts four evaluation indicators of data integrity, data dispersion, data repair ability, and data support degree, and calculates the index weight by the entropy weight method. Taking the geomagnetic data, license plate data, and floating car data of Jinan City as examples and the traffic situation discrimination as the research purpose, selecting the travel speed as the fusion index, the case analysis is carried out to verify the effectiveness of the key data source identification method proposed in this paper.

In the process of recognizing traffic signs with traditional convolutional neural networks, the discarding of the target coordinate frame by the maximum pooling layer makes it impossible to detect the instantiated parameters, such as the pose and the position of object features in real-world scenarios. To solve this problem, a CapsNet algorithm for the recognition of traffic signs based on the Bagging integration is proposed in this paper. Using CapsNet as the base classifier, a novel traffic sign recognition algorithm based on the Bagging integration framework was constructed. By setting an adaptive oversampling rate, the SMOTE algorithm was used to oversample the minority class samples to reduce the imbalance in the samples. Algorithms such as Multi-Scale CNN, Committee of CNN & MLP were selected under the same testing environment to compare and analyze the recognition accuracy of the German traffic sign recognition benchmark (GTSRB) and the multi-view dataset after the affine transformation. In the meantime, by using GTSRB as the training set and the sampled dataset of LISA and Belgium TS as the testing set, the generalization capability of the proposed algorithm was investigated. The experimental results showed that, compared to the optimum public mode, the accuracy of the traffic sign recognition model proposed in this paper was 99.07% and the recognition accuracy on the multi-view traffic sign dataset was 77.58%, which is equivalent to a 7.21% enhancement in addition to a good generalization capability.

A key issue in high-speed automatic driving scenarios, is the recognition of lane change intention of surrounding vehicles. Conventional methods are effective in frequent lane change scenarios. However, considering the variance of individual driving characteristics and measurement uncertainty, false intention recognition remains a major problem to be resolved. We propose an approach to reduce false lane change recognition by introducing the predicted trajectories of the target vehicles. A rule-based approach infers initial lane change intention and a sampling-based approach generates a predicted trajectory according to the inference. Considering the physical inertia, a constant acceleration motion model is used to generate another reference trajectory. The preparatory lane change intention updates in real-time if the predicted trajectory deviates from the reference trajectory. Results demonstrate the approach to improve the lane change intention recognition process.

Frequent lane-change of vehicles can have a serious impact on traffic safety and traffic congestion. In the era of the internet of vehicles, it is helpful to regulate driver’s driving behavior by detecting frequent lane-change behaviors and promptly alerting the driver. In this paper, the accelerometer and gyroscope data collected by the smartphone inertial sensor is being used to analyze the lane-change behavior of the vehicles. The data is being trained by AdaBoost and combined with the sliding time window for frequency identification. The effectiveness of the proposed algorithm is being compared to that of SVM algorithm and BP neural network. The results show that AdaBoost algorithm is superior to SVM algorithm and BP neural network. Finally, the same experiment was conducted with the data collected by the high-precision inertial at the same time. When compared with the results of Adaboost algorithm, the practicability of the proposed method is further verified.

This paper develops an indoor rapid testing platform for AV based on VIL simulation which comprised of four subsystems: a drum-based mechanical test bench, a computer-aided measuremental control system, a software-based scenario and sensors simulated system, and an automatic evaluation system for the testing result. The AV is coupled with the virtual simulation scenarios and the test bench to construct a closed-loop system. In the mechanical test bench, the driving resistance for different road adhesion coefficients and geometries can be simulated. We adopted virtual reality technology to simulate various road traffic scenarios to verify various functions of the AV. This paper selects lane change of an AV as the use case to verify the effectiveness of the test platform. The results show that the AV can efficiently execute the lane-changing maneuver under various conditions on the proposed platform. Compared with actual road test, the test efficiency has been greatly improved.

With the rapid growth of the number of vehicles, the traffic problems of expressway are becoming more and more serious. Vehicular platoon technology can effectively solve these problems and it can also improve road capacity and safety. But the current test of vehicular platoon focuses on 2D scenes lacking 3D scenes which can simulate the real environment. Also, there are too many researches on longitudinal motion control of vehicular platoon while little attention is paid to lateral motion control. Therefore, this paper has completed three main contents: Firstly, the vehicular platoon motion control algorithm is designed and implemented, especially the lateral motion control algorithm. Secondly, the vehicular platoon motion control simulation testing system is constructed and realized based on unity and driving simulator. Through the driving simulator, the throttle, brake, and steering wheel data are sent by serial communication to the vehicular platoon in the unity virtual scene. And then the simulation data during the vehicular platoon movement is collected. Finally, the vehicular platoon motion control experiment is carried out in the previously built simulation testing system. And then the speed running track profile and the vehicle spacing changing profile are drawn to analyze the stability of the vehicular platoon motion control. The test results show that the vehicular platoon motion control algorithm designed in this paper can effectively control the entire vehicular platoon. The speed running track profile and the vehicle spacing changing profile of the vehicular platoon change evenly. Therefore, it indicates that the vehicular platoon motion can be kept stable during the operation. This simulation experiment lays the foundation for the real test of vehicular platoon motion control in the future.

The service reach of bus station, namely service radius, is a key indicator used to measure the quality of convenience services in a bus station. According to the transit capacity and quality of service manual (2nd Edition), the walking service radius of a bus station is 400 m. Based on the big data of Shanghai Mobike and the location data of bus routes and stations, the article uses the latitude and longitude distance formula to calculate the riding distance, and proposes a method for calculating the service radius of a bus station when shared bicycles are used. In addition, the result of verifying stations of Shanghai Bus No. 930 based on the proposed algorithm shows that the riding service radius of each station is greater than the standard walking service radius, which indicates that the use of shared bicycles has significantly improved the convenience service quality of bus stations.

With the development of smart cars, map accuracy requirements continue to increase. Traditional maps can carry limited information from artificial site mapping, which is time-consuming and labor-intensive. Therefore, it is important to create 3D reconstructions quickly and efficiently, and to add more portable information. We propose a 3D map based on real-time reconstruction. An advantage of point cloud data is that it is simple to collect, allowing for real-time collection and reconstruction. Here, we analyze modeling point cloud data through data preprocessing, matching of point cloud data, point cloud integration and segmentation operation, the curve and curved surface reconstruction, and establishing a good model for surface smoothing. The experimental environment adopts KinectFusion accelerated by GPU, which can produce dense 3D reconstructions in real time and display 3D maps quickly and conveniently with Unity. For this time-consuming problem, we use octrees instead of KD trees to optimize map reconstruction.

The traffic congestion becomes a severe problem in almost every city, and intelligent transportation systems make it possible for an adaptive traffic signal control system to improve signal control. Exploiting deep reinforcement learning for traffic signal control is a frontier topic in intelligent transportation research. However, it’s hard to use centralized reinforcement learning for large-scale traffic signal control systems due to the high dimensions of the joint action space. Multi-agent deep reinforcement learning overcomes the curse of dimensions but introduces a new problem: how to learn coordination between agents under a partially observable traffic environment. In this paper, we introduce a multi-agent deep reinforcement learning algorithm for a large-scale traffic signal control system. The proposed method is compared with greedy policy, independent Q-learning method, and independent actor critic method in a large synthetic traffic networks. The simulation demonstrates the proposed method is more efficient than other decentralized reinforcement learning approaches.

Highway work zones can cause traffic congestion and even traffic accidents. Dynamic lane merging (DM) and variable speed limit (VSL) systems are possible solutions to improve the efficiency of work zones. This paper presents a novel integrated control algorithm for freeway work zones under partially connected vehicle environment. The algorithm consists of three parts, i.e., data fusion, VSL, and DM. Data fusion can integrate trajectory data of connected vehicles and traffic flow data of detectors. The VSL algorithm can provide speed limits for non-connected vehicles and speed profiles for connected vehicles. The DM algorithm integrates dynamic early merging (DEM) and dynamic late merging (DLM) and provides a lane-changing scheme for connected vehicles. The integrated control algorithm was implemented and tested using simulation. The results showed that the integrated algorithm effectively decreases the delay under different traffic demands and market penetrations.

Huge multi-source data sets are used in traffic information system research. As a popular indicator, travel time reliability can reflect the time variability and volatility of the transportation system, instead of the shortest path as the priority principle of travel decision-making. The research on how to calculate the index is relatively mature, but the significance and the level of the calculating value have not been researched completely. Based on the historical data, the non-parametric test, and fuzzy clustering are used to model the threshold of travel time reliability. The proposed method is described as the quality of reliability. Under the decision-making scenarios, it is applied for drivers to decide the best departure time and route choice. Finally, the established model is validated by using the expressway of Shaanxi in the case study.

To enhance the driving safety of automated vehicles under adverse weather, the weather processing and analysis system model is taken for automated vehicles based on connected vehicles and the driving strategy could be optimized according to real-time weather. The model uses on-board sensors and traffic information acquisition technology to obtain real-time weather dates and traffic information dates which would be transmitted to the terminal of the vehicle via short-range communication technology. Meanwhile, the instruction from the terminal is fed into an electronic control unit (ECU) to regulate the speed of the vehicle to account for weather and traffic. In addition, the road-side warning devices also provide the recommended speed to enhance the driving stability and safety of automated vehicles. The results show that weather processing and analysis system can apply into automated vehicles to adapt to adverse weather.

To rationally evaluate bus efficiency, the traffic environment of the city must be considered. Based on real time traffic data from the internet and AVL (auto vehicle location) data of bus lines, a fusion dataset can be derived by integrating data spatially and temporally. Two major indexes, bus speed and bus to non-bus vehicle speed ratio, are then calculated and applied in various cases. Results show that bus speed is good at quantifying the absolute efficiency, while speed ratio the relative. By combining both aspects, a bus efficiency evaluation method is proposed. By considering both speed and speed ratio, the absolute, and relative level of bus efficiency can be quantified in a more comprehensive and rational way.

With the development of automated driving, people’s transportation will become more and more convenient. However, there are also some safety problems brought by automatic decision-making, especially in the case of an emergency. It is far from enough to control the car based on the vehicle sensors, but the application of the technology “internet of vehicles” enables vehicles to easily obtain the information of the surrounding vehicles and the road, which brings the possibility of V2V collaborative decision-making and control, and also brings new methods for safe response to complex traffic conditions. In this paper, the two-lane three-car scene is taken as the research object, aiming at the situation that a team composed of two cars needs to change lanes urgently when encountering roadblocks on the original road. Considering the different position and speed of the vehicle in the target lane, different lane-changing control strategies are adopted to achieve the optimal control effect. In the scene construction, based on the technology of vehicle-road collaboration, the two cars perceive the roadblock in front within a certain distance, and then the head car makes the decision to change lanes. After the lane change decision is made, the lane change vehicle and the vehicle of the target lane will transfer the location information to each other and carry out corresponding control. In the process of lane change, the speed of three vehicles will be controlled to achieve a safe the lane change distance. In order to verify the rationality of the scheme, this paper built a simulation platform based on the visual simulation software Carsim and conducted simulation tests for different scenarios. The results showed that under the collaborative control of multiple vehicles, the emergency obstacle avoidance problem in automated driving could be solved more safely and reasonably.

Assuming that all travel demands are met by shared autonomous vehicle (SAV), the paper compares graph theory method and the multiple travelling salesman problem (MTSP) method in solving the minimum fleet size problem. With the trajectory data of 50 new energy private cars in Shanghai for 1 year, travel demands are extracted. A specific method for calculating whether two trips can be served by one SAV is developed. Then, the minimum fleet size problem are transformed into the minimum path cover problem on directed graph (graph theory method) and MTSP shortest path problem (MTSP method). Hopcroft-Karp algorithm is adopted in graph theory method, while genetic algorithm (GA) is adopted in the MTSP method. Results show that graph theory method outperforms the MTSP method both in the quality of solution and the computing time. Results indicate that a SAV can replace 2.5 traditional private cars on average.

Artificial intelligence has developed rapidly and intelligent equipment has improved in the field of urban rail transit. The computer maintenance management system (CMMS) is still used, but cannot satisfy requirements from the internal systems of intelligent equipment. In the intranet, CMMS is only a platform processing based on the Web including information published, reviewed and task approval. However, it has simple logic and high manual repetition rate, which is similar with the electronic data processing system (EDPS) in the early 1950s. Based on the existing CMMS of urban rail transit, the paper analyzes the system requirements from the perspective of multiple scenarios including users, application, data, technology, and management. The information management system of intelligent maintenance equipment accelerates information integration and flow between multiple systems to create intelligent interaction experience between people and information resources. Therefore, it meets the requirements of intelligent maintenance equipment and promotes management efficiency.

With the development of ITS (intelligent transportation systems) and autonomous driving, connections among cars, and the communication between sensors and algorithms are becoming more and more flexible and sophisticated, raising the demand for the compatibility of software platforms. Currently, however, widely applied software platforms like ROS and Apollo only support specific versions of Ubuntu, which limits the communication and transplant among various equipment. Thus, this paper presents a highly compatible software platform named OpenICV (open intelligent connected vehicle), which is deployed and tested on Tsinghua autonomous driving experimental vehicles. OpenICV supports the majority types of mainstream operating systems, such as Linux, Windows, QNX, macOS, RTLinux, and Embedded Linux. The autopilot vehicles equipped with different hardware and operating systems can achieve effective communication via OpenICV. In addition, the autopilot algorithms developed on OpenICV can be easily transplanted among different equipment. A fast and secure communication mechanism is developed on OpenICV.

The primary objective of this paper is to comprehensively exploit and optimize internal and external traffic management strategies based on the macroscopic fundamental diagram (MFD) in congested urban road network. Using taxi GPS data and field data of Tianlin Shanghai, a microscopic simulation model with dynamic assignment module is established. First, this paper explores the network internal optimization strategies concluding the intersection channelization optimization and signal timing optimization. To further improve the network efficiency, the network external traffic management strategy of boundary upstream intersections-based feedback control is designed and combined with the internal optimization scheme. The results show that the network internal optimization strategies are useful but not enough to avoid the occurrence of congestion. Integrating the internal optimization strategies and the network external perimeter feedback control strategy, an ideal control performance that the network is kept working at its critical state can be achieved. Providing a comprehensive traffic management strategies optimization method, this work can be applied to real-time traffic management practice. A further study will concentrate on the dynamic coordination of the internal and external signal control schemes.

The paper presents a control scheme of counter-overflow by use of double detectors to alleviate the problem of overflow at intersections with oversaturated traffic. According to the conservation principle, the running status of vehicles at intersections with saturated traffic and the queuing rule of vehicles under traffic congestion, the paper develops a method of determining the installation positions of the overflow identification and queue dissipation detectors. According to the different status of traffic lights at intersections with overflow and its downstream intersection, two counter-overflow schemes—intercept or evacuate are formulated based on minimum green. Finally, a VISSIM simulation is conducted at two adjacent intersections of Anli Road in Beijing to evaluate the proposed control scheme. Results show that the scheme can mildly eliminate traffic overflow, and make the transition from the counter-overflow control phase to the timer control phase smoothly and thus prevents the drastic changes in traffic flow.

Compared with the ordinary expressways, the ring expressway has more ramps, and most of the ramps connect with the urban roads. This paper seeks to design the structure of a real-time extension controller for the ring expressway on-ramp with the extension control theory and the grey prediction method. According to the deviation and differential of the actual and the expected traffic flow density of the main line, the designed controller divides the main line traffic station into three measure modes and presents different control algorithms to each mode. Finally, a section of Lanzhou North Ring Expressway is used to evaluate and analyze the designed controller, the Simulink software is used to simulate the control process. Results show the traffic density of main line converges to the expected density, the fluctuation of on-ramp metering rate is reduced, and the average queue length of the on-ramp vehicles is shortened.

In this study, we established a real-time control framework of a group of intersections in an over-saturated urban road network using rolling discrete optimization. We examined vehicle queue overflow and waste of green time at key intersections, as well as vehicle departure rate and section length of critical path. Based this data, we established the offset optimization method based on line-axis combination, where the signal cycle is adaptively adjusted with the real-time saturation of key intersections as the constraint. Simulation results show that the line-axis combination method can improve the critical path capacity by 9%, reduce the overall delay of the intersection group by 7%, and effectively improve the vehicle traffic efficiency of the intersection group under the condition of super saturation.

The precise dynamic control of autonomous driving tracked vehicles is very difficult to achieve due to its complexity and high delay, however, it is also one indispensable key performance. In the meantime, according to the characteristics of the complex off-road environment, terrain, and slope are unknown and change violently, which leads to unknown and time-varying steering resistance coefficient and increasing the control difficulty. This paper extracts two key motion variables of vehicle speed and steering angular velocity, decomposes the tracked vehicle motion control problem, designs a control strategy based on model reference adaptive control (MRAC), and thus achieves the adaptive closed-loop control performance. This control strategy predicts the motor load through the vehicle dynamics reference model determined by parameters and adjusts the motor output torque through the adaptive control law so that the actual vehicle speed and angular velocity follow the output of the MRAC reference model’s vehicle speed and angular velocity in real-time. The performance of this control strategy is verified by real vehicle tests in the off-road environment. Compared with the traditional speed closed-loop control strategy, the tracking performance is improved by 10%, and the off-road average speed is improved by 10%.

One of the major application scenarios of military and police unmanned vehicles is to support and carry equipment to reduce the load of personnel. Therefore, personnel identification technology in a complex off-road environment is one of the key technologies of military unmanned vehicles. The effect of laser radar identification is relatively good, but the method of monocular vision can greatly reduce the system cost and facilitate the popularization and application of equipment. In order to solve the problem of personnel identification under monocular vision perception, especially the problem of background interference in complex environment, this paper designs an improved YOLOV3 deep learning network architecture based on monocular vision information, and proposes a YOLOv3 deep learning network architecture with an improved residual module. Based on the original YOLOv3 network RB output, a global pooling layer, two full connection layers, Sigmoid activation function layer, and multiplication layer are added. Through the verification of real vehicles in an off-road environment, the results show that the designed personnel recognition algorithm based on monocular vision can accurately recognize personnel targets in a complex environment, and the recognition rate in a complex environment exceeds 95%.

Regional urban networks are increasingly interconnected due to the continuous flow of traffic, so it is of great significance to establish a reasonable node importance evaluation method in static road network topology. Firstly, this paper proposes to consider the traffic characteristic parameter index, selected six indexes which encircled by node degree, node betweenness, clustering coefficient, road width, traffic flow, and delay. The evaluation indicator system of topological characteristics of the urban road network is constructed. Secondly, based on the grey relation analysis method, the paper analyzed the relational grade of the road network area under the influence of the index weighting and established the evaluation method of the node importance of the road network in the urban area. Finally, the importance of the regional road network is calculated. The introduction of the road network operation index is helpful to the analysis of the topological characteristics of the urban road network and makes it more scientific and accurate to extract roads with a high relational grade in the region, so that traffic managers can take more targeted traffic management measures, which plays an important role in improving the reliability of the road network.

In order to search the appropriate truck fuel consumption prediction method based on the dynamic fuel consumption–speed data from the vehicle network, we first select the speed-related factors which are driver-controllable as fuel consumption influencing factors. After correlation analysis, we establish and implement a prediction model of truck instantaneous fuel consumption, which called the data-driven-based general regression neural network (GRNN) model. And beetle antennae search (BAS) algorithm is applied to find the proper training parameter of GRNN. Besides, three other models are established for contrast: back-propagation neural network based on kernel principal component analysis (KPCA-BPNN), representative of another kind of neural network model; the VT-Micro model, representative of traditional data-driven models; and VSP model, representative of traditional physical models widely used in practice. The results indicate that both two neural network models give out reasonable results better than the traditional VT-Micro model and VSP model. The fuel consumption predicted by VT-Micro model is obviously higher than the actual measurements when the idle ratio is abnormally high. KPCA-BPNN model performs best in fuel consumption prediction, but the KPCA-BPNN model requires excessive parameter adjustment which slows down computational effectiveness. Thus, the BAS-GRNN model with propel calibration and shorter training time is more suitable in practical application.

The transit signal priority (TSP) scheme is a promising method to reduce stop times and improve the travel speed of trams. However, it is difficult to realize the non-stop intersection passing of trams in a long tram line using existing active and passive TSP methods. To overcome this problem, this paper proposes a novel prioritized tram multi-BAND (PRI-TRAMBAND) model that combines the active signal priority (ASP) and green-wave band (GWB) schemes. As a case study, a tram line with a total length of 8.5 km and 39 intersections is designed to verify the effectiveness and applicability of the proposed model. The proposed method realizes the non-stop passing of the designed tram at all intersections and can significantly improve the travel speed of modern trams. Moreover, the influence of the TSP strategy on the road traffic is minimized, making the TSP scheme for modern trams more environment friendly.

Nodes or edges in complex networks may break down because of being overloaded when they are attacked on purpose. The breakdown can spread along with the network and cause a cascading failure. We use the data from a city in the north of China to study cascading failure in urban traffic networks. Firstly, a cascading failure model is built based on existing traffic theory and the simulation software SUMO is used to verify the validity of the prediction model. Secondly, we invent a method to find proper nodes to delete in order to control the spreading of cascading failure. During experiments, we find the nodes’ importance in cascading failure is related to their betweenness and certain relationships.

The GPS map matching technique matches a series of geographic coordinates to a road network. However, most existing algorithms mainly consider the geometric and topological relationships of the GPS observations, disregarding information in the historical floating data. This study developed an enhanced spatial-temporal matching (EST-matching) algorithm effective with low frequency GPS data. The proposed algorithm uses the possible minimum travel time to filter out the unrealistic route and build a graph of the candidate paths. (2) It considers the initial matching probability between candidates and the historical edge data to identify the actual vehicle route. (3) For vehicles at intersections, we introduce direction analysis to increase algorithm accuracy. The EST-matching algorithm was tested against the stMM algorithm to verify its performance at various data collection frequencies and matching radius. The proposed algorithm outperforms the stMM algorithm in terms of matching accuracy based low sampling frequencies, especially in central urban areas.

With the rapid economic development of China’s big cities and the continuous increase of urban passenger flow, how to guide the safe, and efficient evacuation of personnel in the case of large passenger flow, it has become the focus of attention in the industry. The evacuation behavior of passengers is affected by their own physical and psychological factors as well as the external environment. During the evacuation process, panic is an important factor affecting people’s behavior; people’s panic state is not just a static fixed value, but a transitive and iterative process. First, logistic regressions was performed on data in the social questionnaire, and get the coefficients of various impact indicators. Second, based on system dynamics, established a panic diffusion model during the evacuation of the bus passenger station, and the degree of influence of various factors on the evacuation capacity of the bus passenger station is obtained using this model. Finally, the sensitivity analysis of the model is carried out to obtain suggestions for the evacuation process of stations, which provides a reference for optimizing emergency evacuations in the future.

Intelligent passenger guidance systems are able to improve travel experience for passengers, and to enhance airport operating efficiency. In this paper, an airport arrival passengers trip chain generating method based on multiple spatiotemporal traveling segments is proposed. Trip chain is divided into two spatiotemporal segments: in and out of the airport terminal building. For the in-door segment, the cost time is calculated with walking time between different infrastructures and waiting time in the transit. For the segment of out-door situation, carrying time on carriers has been estimated. BPR model and queue theory are applied to estimate the walking time, and BP neural network is adopted for carrying time. A passenger guidance platform is built to verify the proposed method, and the real scenario test is conducted. It’s found the method can accurately estimate time cost comparing with passenger historical data, and the optimized trip chain can notably reduce passenger travel time.

With the intelligent vehicle-infrastructure cooperative system (i-VICS), a connected and automated vehicle (CAV) can obtain the precise vehicle location information via vehicle to infrastructure (V2I) communication to accomplish the path tracking task. However, in some special scenarios, the location information of vehicles might become inaccurate, e.g., the vehicle failed to communicate with the road-side unit (RSU), which decreases the stability of path tracking. In this paper, we focus on improving the reliability of vehicle location information during tracking path. First, a predicted method using vehicle dynamics is proposed to recognize the scenarios in which the vehicle location information becomes inaccurate when the vehicle moves. Then, a modified Kalman filter is employed to adapt to a more complex scenario with variable position error. At last, simulation results demonstrate that the proposed method has a marked effect on handling the driving scenario with inaccurate location information.

Credibility reduction of vehicle-to-everything (V2X) communication would significantly influence the safety and performance of an intelligent transport system (ITS). For interactive information credibility enhancement during V2X communication, this paper first proposes a vehicle state optimization method based on communication delay. Then, considering comprehensive credibility, a vehicle state accuracy evaluating method is presented to judge the optimization results. Bayesian regularized BP neural network is designed for efficient position prediction with a high level of accuracy achieved. Testing sets composed of realistic data and communication delay error models are established to verify the performance of the proposed optimization method. The results show that compared to the realistic data, the proposed method can reduce the differentials between received vehicle state and realistic vehicle state. Furthermore, the credibility of the communication can be improved and vehicle safety can be guaranteed.

Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs) and their nearby intersections. The standard preemption provides little or no consideration to pedestrian safety, or traffic signal efficiency of signalized intersections near highway-rail grade crossings. To solve these issues, a case study was conducted, and developed a simulation-based optimization methodology for estimating traffic signal timing plans on arterial corridors with multiple intersections near HRGCs. Genetic algorithm (GA) was used as the optimization approach, and a new transition preemption strategy for dual tracks (TPS_DT) and a train arrival prediction model were integrated in the proposed methodology. A sensitivity analysis with different train traffic scenarios was conducted. Measurements of effectiveness (MOEs) used for evaluation include rate of pedestrian cutoffs, intersection vehicle delay, and corridor vehicle delay. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with multiple intersections near HRGCs.

Raw data quality control is a research content of data preprocessing in Intelligent Transportation Systems (ITS). In this paper, a time series pattern extraction and confidence interval estimation based outlier detection strategy is proposed for traffic flow outlier detection. Analyzed traffic flow data includes sectional traffic volume and average speed series with 5 min time interval was collected from urban arterials in Kunshan City, China. Fluctuation characteristics of traffic flow time series are analyzed, then an STL model proposed by Chatfield is used for traffic flow time series decomposition and wave pattern extraction. Taking the extracted series pattern as modeling objects, an ARIMA model is introduced to estimate the dynamic confidence interval of the pattern series. Finally, the outliers of traffic flow time series can be identified and kicked off from the estimated dynamic confidence interval. Results show that the proposed strategy can detect the outliers of traffic flow time series.

In order to improve the speed of vehicles locating at intersection, this paper proposes a method based on two-dimensional projection vehicle model (TPVM) and optical flow. Firstly, the transformation model between road coordinate system and pixel coordinate system is established on the basis of the internal and external camera parameters which got by off-line calibration. Secondly, the TPVM in the road plane is founded according to the corner points of the vehicle, with the discarding of height information of the vehicle. The coordinates of the model’s corner points in the road plane are determined by the former transformation model, according to the pixel coordinates of the corner points belong to this model. Furthermore, to determine its coordinates in the road coordinate system at any time, the TPVM was tracked by optical flow. Finally, the experiment is designed to verity the locating accuracy and speed of this method. This experiment compared the two factors of TPVM method proposed this paper with those of two existing methods. The result shows that the errors of the three methods are 60.45, 74.24, and 66.34 cm respectively. Meanwhile, the locating speed of TPVM method increase 20.33 frames and 12.47 frames compared to the two other methods, separately. The experiment proves that the method can improve the speed of vehicle locating at the intersection in the premise of ensuring the locating accuracy.

Video vehicle detection is more valuable and challenging than image vehicle detection for an intelligent transportation system. Due to the existing situation of vehicle blurring, occlusion, and scale changing in traffic monitoring, using static vehicle detection network often leads to the decrease of detection accuracy. In this paper, based on DFF method, we fuse a tracking algorithm to realize box propagation, and form a new video vehicle detection framework that considers both detection accuracy and speed. We extract feature maps in key frames, and propagate feature maps and boxes in non-key frames. Compared with static detectors, the proposed method greatly improves the consistency of video vehicle detection results. In addition, the detection performance of our method is obviously superior to the basic detector DFF in accuracy.

Basing on the real-time dynamic data in condition of connected vehicles, the paper optimized the BPR impedance function model, built a more reasonable control model on the real-time dynamic reversible lane. For avoiding the conflict between light and heavy traffic flow, a buffer zone setting model is proposed, which improves the operation safety of the real-time dynamic reversible lane. Finally, the scheme was verified through VISSIM simulation by taking Xiangyu avenue of Huai’an City as the example. The results show that, for the 3.2 km section, the average travel time in the direction of heavy traffic flow decreases by 3.4%, and the total travel time decreases by 550.8 min after using the real-time dynamic reversible lane, while the average travel time in the direction of light traffic flow is basically the same. The real-time dynamic reversible lane could not only overcome the shortcomings of traditional tidal lane, but also reduce the total travel time of vehicles effectively, and greatly improve the traffic efficiency of the road network.

Pavement crack images typically have the characteristics of uneven distribution of illumination, strong noises, and a small proportion of cracks. Differentiating the cracks from the background image with the traditional grayscale analysis and edge detection methods is difficult. To solve this problem, an algorithm based on multi-scale shape analysis was developed. The algorithm divided a whole pavement image into 256 image cells. For every cell, an optimal threshold was selected to become a binary image. Under small scales conditions, 6 shape-factors were extracted from the binary cell image which composed a feature vector that was inputted into a BP neural network to classify the binary cells into two types: crack or non-crack. Lastly, under the conditions of big scales, the “wild spots” were deleted, and the cracks were located precisely on the whole image. Experimental results show that the proposed algorithm has an advantage in computing speed and accuracy rates.

With the development of artificial intelligence and communication technology, road-vehicle fusion technology has become the trend of future traffic development. High-precision trajectory planning is required for the operation of autonomous driving vehicles. This paper focus on the trajectory planning problem for autonomous vehicles driving in the region where objects are occluded. At first, the space-time map and Frenet coordinate system of the road are established. Through cooperative perception between the autonomous driving vehicles and infrastructure system, the platform of road analyzes the potential risks. Hybrid A* path planning improved for the speed planning generates the optimal trajectory. The proposed framework is implemented through simulations in accident-prone scenarios in this paper. The simulation results show that the trajectory and speed smoothness of the autonomous driving vehicle is improved, and the driving safety of the autonomous driving vehicle is enhanced in the road-vehicle sensing cooperative system.

Understanding path choice behavior is important for accurately modelling and estimating traffic distribution. However, large scaled path choice data is usually difficult to obtain. Most current studies use survey data in a relatively small sample. This paper analyzes path choice behavior based on trajectory data obtained by license plate recognition from cameras. Data from Mianyang on September 1, 2018 is used in this study. Raw data includes license plate, vehicle type, time, and camera serial number. Trajectory data is obtained after data processing. Results show a surprising path choice preference, especially during the peak period. Multiple paths are shown in trips between a same OD pair, which can possibly be explained by the fact that residents in a middle size city often have multiple activities connected during peak hours. This finding may provide a new angle for modelling traffic distribution in small and medium cities.

Passenger transportation hub is important for the urban transportation network. However, it’s difficult to get the passenger flow pattern within the hub station using traditional method, such as video detection. Mobile signaling data, as a kind of big data provides a solution for this problem. In order to improve the accuracy of extracting travel information, we need to address oscillation sequences and locational uncertainty in mobile signaling data first. And then, we can obtain the travel information. Finally, we use the earlier proposed method to extract the trip information and analyze the temporal and spatial distribution in Beijing West Railway Station, and verify the effectiveness of the method. The method proposed in this paper is beneficial for hub managers to understand the characteristics of passengers’ flow and effectively carry on transportation tasks and so on.

With the increase of urban scale and rapid development of transportation, intercity travel is more convenient and plays a crucial role in the urban transportation system. Traditional travel survey methods are difficult to apply to the research between hubs. This paper proposes a method based on mobile phone data to calculate the passenger travel OD between the Beijing-Tianjin-Hebei urban megalopolis hubs, and analyzes the spatial and temporal law of passenger travel. Firstly, we propose an approach to extract passenger trips information among hubs from mobile phone data. Secondly, we describe the spatiotemporal travel characteristics of passengers at the Beijing West Railway Station—Zhengding Airport. The results show that mobile phone data has great potential for studying passenger travel among megalopolis.

Travel time information serves as a basis for travel planning, route guidance, and congestion avoidance. The advances of connected and autonomous vehicle (CAV) technologies offer vehicles the potential of reduced travel time compared to conventional human-driven vehicles (HDVs). In the future, roads will be shared by both HDVs and CAVs, leading to mixed traffic flows that can significantly differ from the single-class HDV traffic flow. Exploring the characteristics of travel time with mixed traffic is essential for better traffic operation and management. In this study, travel time distribution was investigated along an arterial road with three continuous intersections established by VISSIM microsimulation. A series of simulation experiments were conducted, and a finite mixture of regression models was used to characterize the mean, variance, and mixing weight of different components. The impact factors such as traffic volume, cycle length, CAV penetration rate, and driving behavior variables were all scrutinized.

In order to alleviate urban traffic pressure and improve road service level, this paper proposed a new traffic signal timing scheme based on video recognition technology, which dynamically adjusts the traffic light time by detecting the number of pedestrians waiting at the intersection. At the same time, a comparative simulation experiment with Vissim microscopic traffic simulation software was carried out based on the fixed timing scheme currently adopted at the intersection and the new dynamic timing scheme. The experimental results show that the optimizing approach proposed here can on average shorten the delay time and the maximum queue length of vehicles by 29.13% and 32.93% respectively, and the delay time of pedestrians by 12.03% in pedestrian rush hours as well.

In order to generate test cases covering complex and varied real-life traffic in a scenario-based test, multiple real samples of vehicle cut-in scenarios were extracted from highD dataset. A description model of vehicle cut-in scenario was built based on analysis of the positional relation between motion parameters and participated vehicles from these real samples. The risk degree of this scenario was evaluated depending on TTC in cut-in point. Finally, test cases generation was executed using Monte Carlo method by combining with the distributions of parameters in description model. The results show that the generated cut-in test cases are capable of covering all risk levels, which can better support autonomous driving testing.

Poor recognition and prediction of near-miss collision risk can lead to catastrophic incidents. Several methods have been proposed to support decision making related to ship-ship collision risk prediction. Most of them intend to analyze and predict one or more ship navigational parameters. This points to an urgent need for improvement of existing methods. As reasonable collision risk prediction is necessary for decision making, this relies on optimal navigation data and an accurate prediction algorithm. In this paper, the extended Kalman filter method is introduced, which provides the next optimal navigation data for ship-ship near-miss collision risk analysis. Thereafter, according to the collision risk level, the near-miss for the next two moments can be predicted by means of the unscented Kalman filter method. The results indicate that the present work can accurately classify the collision risk level for two-vessel encounters and mitigate the human judgment error.

3D object detection plays an important role in autonomous driving, which provides 3D location information of objects for subsequent decision-making modules. Existing 3D object detection algorithms can be divided into three ways: lidar-based, stereo image-based, and monocular image-based methods. Lidar-based methods depends on large and expensive lidar sensors to provide depth information which largely increases expense. Stereo image-based method mostly uses stereo images input with multistage networks which causes large computation cost thus limiting their using scenarios. Meanwhile some scholars proposed methods like Deep3DBox which only utilize monocular image input and can obtain competitive precision. However, their lack of depth property brings about unstable performance. To deal with that, we propose a novel method which uses both monocular image and cascade geometric constraints to obtain robust detection. The framework is divided into two stages. The first stage processes the monocular image input using key points-based detection network CenterNet with additional branch to regress the orientation, dimension, and center projection of bottom face. In the second stage, increasing IOU threshold can filter out unprecise 2D bounding boxes which cause performance degradation. After that cascade geometric constraints are utilized to obtain the final 3D box output. Our framework doesn’t depend on any external sources or subnetworks and can be trained end to end. We tested the proposed method on the KITTI-3D benchmark to test its ability and efficiency.

In the environment perception technology of autonomous vehicles, the dynamic sensing and tracking of vehicles are basic and key step. In order to improve the efficiency of vehicle perception in the traffic environment, this paper proposed a smart terminal car license plate perceptual method. The Mobilenet-SSD model is used to locate the position of the position of car and car license plate. The OCR algorithm is used to recognize the character information on the car license plate. The experiment found that using EDSR preprocessing method to increase the resolution of image for poorly captured car license plates can also improve the effective perception rate of the perceptual model. In addition, the transmission and feedback of video frames can effectively reduce the computing cost of vehicle sensors and improve the level of environmental perception and understanding of autonomous vehicles.

Traffic environment perception is the foundation of autonomous driving, and road type recognition can provide support information for decision making of the intelligent vehicle. In this paper, we adopt the strategy of integrating the Mask R-CNN model with two different convolutional neural networks (ResNet-101 and ResNet-50) to perform the road type recognition and establish road type dataset containing pictures of the expressway and urban main road based on the TT100K dataset. The experiment results indicate that Mask R-CNN with ResNet-50 can achieve the mAP of 96.30%, which is 17.60% higher than that of ResNet-101, and can realize the recognition of road type effectively. Also, the comparison between ResNet-50 and ResNet-101 demonstrates that for different recognition tasks with different amounts of data, suitable depth network needs to be chosen carefully in order to achieve satisfying results.

The autonomous driving era is coming. As we all know, autonomous traffic flow systems have different properties compared with man-controlled ones. Therefore, in this paper, in order to discover the difference between them, we present a discrete sampling control model with zero-order hold for autonomous traffic flow systems. We first analyzed the main components of an intelligent car to understand its mechanism, then we built a continuous model based on Bando’s OVM. Thirdly we brought out the discretion method for the continuous system’s transfer function by using the z-transform. Finally, we analyzed the stability and transient response of the system by computer simulation. The numerical simulations results verified our theoretical supposition.

Schedule management is an important part of project control, which is closely related to resource utilization and project economic benefits. The traditional schedule management mode has disadvantages of poor coordination, many influencing factors, no dynamic, and poor integrity. BIM Technology is introduced into project management, which provides a new idea for controlling project schedule. BIM includes the information of the whole life cycle of the project. Based on BIM, this can provide data support for the progress management in the construction stage and realize the dynamic management of the construction progress through real-time updates of progress data. In the construction process of the project, it is particularly critical on how to use more advanced technology and methods to carry out scientific planning and effective control, enhance the coordination and cooperation between various disciplines and types of work, and build a connection structure and information integration system based on BIM Technology.

Short-term passenger flow prediction plays a pivotal role in metro operations. Based on the predicted results, metro operators can develop feasible strategies for dynamic train scheduling and passenger flow controlling in peak periods. To gain precise prediction results, a considerable number of deep learning models have been applied. However, the prediction has its intrinsic difficulty for high volatility in small time granularity and the prediction accuracies and stabilities of these models are limited. In this paper, a long short-term memory neural network (LSTM) based architecture is proposed with the purpose of volatility reduction and precision improvement in short-term passenger inbound flow prediction. The framework combined with high correlated stations data and temporal characteristics to ease data volatility. The case study in Guangzhou implicit that the proposed LSTM-based architecture outperforms the simple RNN, GRU, and LSTM, which achieves a high prediction accuracy of 92.15%. It is a reasonably feasible method to address the data volatility problem that exist in the domain of short-term passenger flow prediction based on deep learning. Metro operators can thus effectively allocate resources to areas with unbalanced proportion about transportation resource for service improvement.

Dedicated short-range communication (DSRC) technology can provide the driver with information about other vehicles beyond the normal field of view, and the DSRC-based forward collision warning system can provide voice warning to the driver to prevent traffic accidents. Due to the positioning error and information delay, the system can trigger too early or too late, which affects driving safety. Considering the impact of delay, positioning, and other factors on collision warnings, we propose an improved collision warning model based on DSRC and differential global position system (DGPS). In order to calibrate the model parameters, we perform communication and positioning tests on DSRC equipment performance. Finally, the simulation test proves that the method has higher warning accuracy, and also proves that DSRC and DGPS can be used for vehicle forward collision warning.

This paper investigates a de-noising sequential data assimilation system and applies it into short-term traffic flow prediction. Models in traditional sequential data assimilation system are usually constructed using historical measurements. They always disturbed by local noises. Simultaneously, the accuracy of assimilation results will be affected. Developed de-noising sequential data assimilation system can separate measurements noises based on empirical mode decomposition to reduce their influences on model and assimilation results accuracy. And then applications into short-term traffic flow prediction using de-noising sequential data assimilation system and traditional sequential data assimilation system are presented. Experimental researches are based on the traffic flow measurements collected from a sub-area of highway between Liverpool and Manchester, England. Results indicate that de-noising sequential data assimilation system can successfully reduce effects of measurements noises on model construction and improve the accuracy of short-term traffic flow prediction when compared with traditional sequential data assimilation system.

This paper proposes a new nonlinear longitudinal controller for connected vehicle platoons with a focus on the incorporation of throttle opening angle. In particular, the dynamic model is used to formulate the characteristics of the vehicle. Then, a new longitudinal controller is developed by considering the car-following interactions between vehicles and position and velocity difference as well as throttle opening angle difference with respect to the immediately preceding vehicle. Finally, PreScan-based simulations are performed to verify the effectiveness of the proposed controller regarding the velocity and acceleration/deceleration profiles.

The driverless vehicle or intelligent vehicle (IV) that has got several functions combined such as environment perception, planning and decision making, operation and control, and so on. They also use various sensors like cameras, laser radars, GPS to percept the surrounding environment in real-time to extract the obstacles around the driving vehicle and then pick out the available areas so that the vehicle can move on successfully. This paper briefly introduces several general methods concerning the sensors mentioned before and explores the research actuality and findings in environmental perception. Finally, it concludes that methods based on laser radar turn out to be more fruitful thus will be widely used in the future.

Research on complexity of environment and driving task is critical in testing and validating L3+ autonomous vehicles (AVs). We define the complexity of environment and driving task of AVs as the sum of uncertainties in the prediction process and propose a method to quantify it. Based on the analysis of the effect factors of perception system and the data processing algorithm of the cognition system of AV, and combining with complexity theory and the definition mentioned above, several types of environmental complexity factors liked the amount, variety, and path of motion of objects, the environment conditions are abstracted. Multi-factor analysis quantification is used to generate a quantifying model for the complexity of environment. A survey among five experts in the field of autonomous driving is conducted. The consistency between the average survey results and the quantification by using this methodology is 81%, which proves the effectiveness of this methodology.

With the popularization and application of domestic steel structure bridges, various diseases will increase, and the creep phenomenon of ordinary concrete curved beams is also inevitable in steel structures. This paper firstly analyzes the influencing factors of creep caused by the continuous climbing box girder of an interchange bridge in Guangdong Province. And using finite element modeling to analyze the influence degree of each influencing factor on the deviation of curved bridges. The results show that the temperature gradient of sunshine and the centrifugal force of the car are the main factors affecting the creep of the structure, while the overall temperature rise is the inducing factor of creep. Secondly, it is explained that the creeping mechanism is due to the deviation of the overall heating structure, and the residual displacement occurs under the temperature difference of the sunshine and the load of the vehicle, which leads to the lateral displacement of the beam cannot be restored. In severe cases, the bearing will be damaged and the normal use of the structure will be affected. Finally, according to the analysis results, combined with the application of actual bridges, the corresponding maintenance, and repair measures are proposed, which have a certain reference function for the design and maintenance of similar curved composite beam bridges.

Increased heavy truck traffic on expressways has raised concerns about safety and capacity in China. Expressway with separation of cars and trucks and exclusive car ramps (ECR) were designed to address these concerns. Currently, there are few studies on design indicators of ECR. Here, we propose a novel calculation model of the widening value for the ECR according to vehicle body movement theory and Ackermann steering geometry. MATLAB software is used to calculate the recommended widening value of the ECR based on the midline radius of the lane and the relationship between the radius and the widening value. The results of this paper are concise, easy to use, and will be a key reference for future designs of ECR.

In the last three decades, prestressed concrete box girder with corrugated steel webs (CSWs) has increasingly become one of competitive bridge superstructure types. It attracted more and more engineers all over the world, and the design theory associated with it was established and gained constant development in practice. Due to the shear deformation effect in CSWs, a non-negligible additional deflection often occurs under concentrated or uniformly distribued loads, as different from conventional box girders with concrete webs. To makte the additional deflection clear in essence, the paper presents a unified analytical expression and then performs an influencing parameter analysis. Results show that the depth-span ratio is the dominating parameter. If the additional deflection is to be limited to a small level and even negligible, the depth-span ratio needs to be designed to approximate to a very small value or a threshold value. However, there is still not a consistent conclusion in the threshold value in the current research, and the threshold values presented in the limited studies need to be further verified. Under these circumstances, the influence of the shear deformation effect in CSWs on the additional deflection has to be checked carefully and then considered adequately in construction control.

The comfort of a passenger is related to vehicle speed and line condition, which represents the comprehensive value of acceleration perceived by passengers. Shanghai Maglev demonstration line is the longest test line in the world, and its mainline is less than 30 km, which cannot support the test or operation of the maglev system with a speed of more than 500 km/h. Therefore, it is necessary to scientifically predict the comfort parameters of the maglev system operating at higher speeds through the existing test data when the test conditions are not available. Therefore, it is necessary to scientifically predict the technical parameters of the maglev system operating at a higher speed through the existing test data when the test conditions are not available. Based on the passenger comfort test data of 430 km/h high-speed maglev vehicle on Shanghai Maglev demonstration line, the relationship between the comfort measurement value and historical data such as running speed, line, and vehicle condition is studied. The relationship between the weight of the relevant factors affecting the vehicle comfort and vehicle speed is analyzed. The improved Elman neural network algorithm is used to predict and evaluate the comfort of 600 km/h vehicle by taking the speed and line condition as input value and the comfort of a passenger as an output value. The above research provides theoretical support for the design of a maglev system operating at 600 km/h or even higher speed.

Permeable asphalt pavements are rapidly being developed and applied for urban permeability, but its bearing capacity for traffic loads is insufficient. Aiming to solve this problem, a new kind of porous water retaining subbase was adopted to replace the traditional permeable subbase. Four typical coastal cities in South China were selected to analyze the rainfall pattern characteristics, and one of them was taken as an example for detailed discussion. According to the rainfall pattern characteristics in this region, the structure combination schemes of permeable asphalt pavement were put forward, and then a suitable pavement structure was systematically designed to satisfy the requirements of permeable capacity and bearing capacity. The results show that under the rainfall condition in this region, the minimum thickness of permeable subbase without surface runoff on the road surface should be 250 mm, which is suitable for urban roads of medium traffic levels.

The London cycle superhighway is always criticized as “nothing but blue paint”. Some people even suggest it will decrease traffic efficiency. This paper analyzes the impacts of the London cycle superhighways on the adjacent bus operation speed using a propensity score matching method (PSM). We include 43 cycle superhighway segments as the treated group and 208 control segments in this research. The live bus arrival time is collected to calculate bus travel speeds. Those arrival time data can effectively guarantee the accuracy of the outcomes. The variables affecting bus operations such as traffic flow, bicycle volume, economic situation, population, and employment are deployed in this research. The experiment is conducted based on six time periods which are morning peak hour, evening peak hour and non-peak hour for both weekday and weekend individually. We further divide the treated samples into two sub-groups which are segregated and non-segregated cycle superhighway segments. Both groups have an operation speed higher than the control group. But it shows that the non-segregated buses are faster than the segregated ones, which is due to the fact that buses near non-segregated segments are always in the non-central area and having larger station spacing. We compare the bus operation speed under different bicycle volume conditions. It also suggests that a higher bicycle volume is related to a lower bus operation speed. After a single factor analysis, the PSM is deployed to conduct a multiple-factor analysis. However, the results show that there is no significant difference between the treated group and the control group, which means CS facilities have neither promotion nor demotion effect on buses operation speed.

The higher the road grade, the higher the safety standards considered in design, and the higher the maintenance standards used. However, the management mode with fixed road section as the maintenance unit does not fully consider the unsafe situation caused by the combination of road surface and geometry design. The low maintenance standards of low-grade roads combined with poor geometry design and complex driving environment make low-grade roads a high-risk area for traffic accidents. Based on the 5-year (2010–2014) accident data of mountainous highways in Chongqing, the comparative attribution analysis of expressways and general highways was carried out. Taking curve section as an example, a safety model based of force analysis without the risk of slippage is established. Skid number (SN) is used as the anti-skid performance index. With different road radius and different pavement condition (skid number), the critical safe speeds without slipping are calculated. The calculations have verified the trend that the lower the road grade, the higher the requirements for anti-skid performance, and the impact of anti-skid performance on safe driving speed is proved significant. Comparing to the requirements of road maintenance standard, the maintenance standard of low-grade roads cannot meet their basic safety requirements. It is believed that the maintenance unit of the road should be defined according to the driving environment and geometry conditions, and the anti-skid performance should be added to the safety audit and the road maintenance standard.

In this paper, the grey comprehensive correlation coefficient is used to determine the weights for cluster analysis. A full-link network model of distresses of bridge structures and bridge deck pavement is established, and the grey comprehensive correlation coefficients between each distress type are obtained. The cluster analysis method is used to group the bridge deck pavement distresses. The structural damage trend was estimated with a feedback prediction, and some suggestions were proposed to avoid the risk of serious structural damage. Through calculation analysis and verification of the corresponding test data, it can be concluded that the weighted clustering method based on gray correlation weighting is feasible for the correlation analysis and trend estimation of bridge distress types. This method can help do pre-judgment for hidden structural damage, and provide a reference for the next step to establish a structural damage process prediction model and bridge intelligent management.

The selection of design parameters has a direct impact on the determination of structural layer thickness and pavement material in asphalt pavement structure design. In the context of most countries along the “Belt and Road” adopt French specifications, the acquisition and calculation methods of design parameters in the asphalt pavement structure specifications between China and France were studied. The difference between environmental parameters, traffic parameters, soil modulus, and material modulus of two countries’ pavement structure specifications was obtained. According to the asphalt pavement structure specifications of China and France, the design parameters of a section of the national road of RN34 in Togo were evaluated. The form of the road structure layer and the thickness of each layer were calculated according to the design parameters. The comparison and calculation results show the differences in pavement design parameters involved in the two countries’ specifications.

Waste engine oil was used to regenerate aged SBS modified bitumen by melt blending. The physical properties of waste engine oil regenerated SBS modified bitumen was evaluated by physical properties tests. The regeneration mechanism of waste engine oil on aged SBS modified bitumen was discussed by FTIR analysis. The results show that the waste engine oil shows a good regeneration effect on aged SBS modified bitumen, but excessive amounts of waste engine oil will adversely affect the physical properties of SBS modified bitumen. The proper amount of waste engine oil in the aged SBS modified bitumen is 10%∼12.5%.

How to reasonably allocate the limited maintenance funds, choose the durable and effective maintenance measures at a reasonable time, avoid the project selection and the investment decision error, give full play to the maximum economic and social benefit of the road maintenance investment, are the common problem that the national highway maintenance and management department needs to solve urgently. On the basis of Jiangsu expressway as the research object, the highway pavement basic database is established, based on the road network detection data of 5-years, the typical major and intermediate maintenance and preventive maintenance measures in Jiangsu Province is analyzed and summarized, and the short-term, long-term, technical benefit indexes are comprehensively considered to determine implementation effect, the curing time, and applicability of each typical maintenance measures. Implement appropriate maintenance measures to make scientific maintenance decision at the right time for the proper pavement, and provide the decision-making basis for the development of medium and long-term maintenance planning and maintenance investment strategy for the highway maintenance management department.

This study constructed driving simulation scenarios in order to determine the influence of a spiral tunnel’s radius on a vehicle’s operation speed and trajectory deviation at the design stage, and potentially reduce driving risk in spiral tunnels. Based on the 3D simulation cockpit and UC-win/Road software, got the corresponding driving speed and trajectory offset data under different working conditions, and the influence of spiral tunnel radius on vehicle operating characteristics under different working conditions were obtained through SPSS multiple nonlinear regression analysis. The research results show that as the radius increases, the speed variation coefficient gradually increases, and the vehicle lateral offset gradually decreases. Finally, based on the lateral offset limit value and the “self-explanatory” theory, the limit minimum radius, and recommended radius value range corresponding to the four working conditions are obtained.

Unconfined compressive strength (UCS) with 90 days of curing time is always applied to estimate the long-term mechanical performance of the cement-stabilized macadam (CSM). But some strength prediction models are often not feasible for the diversification of mixtures. So the UCS tests were conducted for considering the increasing utilization of recycled aggregates (RA) and early agent. Then, this study established a relationship between 90d UCS and the time-strength data easily obtained, and the feasibility was determined by deviation and regression analysis conducted between calculated and tested values of CSM mixtures. The computed determination coefficients reach up to 94.7, with a maximum error (4.6%). And the calculations also greatly agreed with the other experimental results randomly selected from some publications. In conclusion, the results obtained were encouraging to use this model, with the possibility of a reduction in test volume.

The Highway is an important infrastructure of the country, and highway asset evaluation is also an important part of highway asset management system. The full assets of highway facilities include all highway infrastructural assets, such as pavement, subgrade, structure (bridge, tunnel, and culvert), and ancillary facility (medial divider, railing guardrail, greening, sign, and marking, etc.). Through the comparative analysis of the commonly used highway asset value evaluation methods, the replacement cost method is selected to calculate the asset value of highway facilities. The evaluation method of the whole assets of highway facilities is studied from three aspects: technical condition evaluation, performance prediction model, and method of asset value calculation. Developing asset evaluation and data analysis of the whole assets of national and provincial roads can make investment decision-makers award of the annual change of the total assets and provide a theoretical basis for making the most scientific and reasonable decisions such as medium-long term plan, annual maintenance plan, investment strategy and implementation plan, and so on.

To quickly evaluate the inner condition of the asphalt pavement, the high dynamic ground penetrating radar (GPR) was used for detection at 80 km/h. Aiming at three kinds of inner distresses, that is, uncompacted, interlayer voids, and structure lose, the radar evaluation fingerprint database was established, and the coring verification was carried out. A comprehensive evaluation index, inner pavement condition index (IPCI), is proposed and is verified through the 11 high-speed in Jiangsu Province. The results show that the accuracy of coring verification based on radar detection fingerprint database is about 95%. The traffic volume has a double logarithmic relationship with IPCI, and the maintenance history has a semi-logarithmic relationship. The larger the traffic volume and the less the amount of maintenance engineering, the lower the IPCI score. GPR can be used for quantitative evaluation of pavement structural integrity to provide decision support for pavement maintenance.

To quickly obtain the design parameters of subgrade modulus, the traffic speed deflectometer (TSD) that was validated by Beckman beam was used. The finite element software was used for 3D modeling with consideration of asphalt layer viscoelasticity and moving load. The influence of pavement modulus on the deflection basin was analyzed. Based on the Boussinesq semi-space theory solution, the subgrade surface modulus was back-calculated. The theoretical back-calculation accuracy was analyzed, and the case study was performed. The results show that the TSD has a significant linear correlation with Beckmann beam, and the coefficient R² is 0.94. The change of pavement modulus has little effect on the outermost deflection. The average theory back-calculation error is 1.9%. The example verification shows that the calculated value of subgrade modulus based on AASHTO 1993 is large, while the back-calculation values based on the model proposed in this paper are close to the design values.

The post-evaluation of highway preventive maintenance engineering can test the quality of preventive maintenance project, on the other hand and also can evaluate the technical, economic, and environmental effects of preventive maintenance engineering, sum up experience, draw lessons, and provide reference significance for follow-up preventive maintenance engineering. Taking the preventive maintenance project of expressway in Shandong Province for 5 years as the research object, the database of pavement assets is established, including the pavement structure, traffic volume, maintenance history, and road condition detection data of preventive maintenance project for 5 consecutive years. From three aspects of technology, economic and social effect and environmental impact, the corresponding post-evaluation model is established to evaluate the implementation effect of preventive maintenance engineering, to determine the best maintenance time of preventive maintenance measures, and to provide theoretical basis for the scientific implementation of preventive maintenance engineering.

The towers of the Yangmeizhou Bridge is a single-pillar shape and its height is 181 m, which is the highest building in Xiangtan City. This research focuses on several significant problems during construction, including how to prevent bored pile hole collapse when excavating large karst caves, how to improve piling efficiency of composite piles when excavating cobbles, how to assemble a large-diameter and double-wall steel cofferdam in deep water, how to control the temperature of mass concrete in summer, how to apply climbing formwork according to variable sections of towers, and how to install cable ducts and steel anchor beams with high precision. These findings provide a key guide and reference for similar bridge engineering projects.

Complete vehicle coverage trajectory data is of fundamental importance to traffic signal control strategy and intelligent transport system (ITS). There are mainly two categories in traffic data collection: stationary and mobile sensing. However, neither can offer near-complete spatial and temporal coverage, especially at traffic signals. This study first proposes a full-scale automated and connected UAV (AC-UAV) system, which consists of transformable UAVs with automated landing and take-off capabilities, cooperative charging piles, and a fleet management center. Secondly, based on the developed AC-UAV system, we introduce a 3-step methodological framework: multiple vehicle detection algorithm (MVD) based on deep learning and multiple vehicle tracking (MVT) algorithm based on data feature association and trajectories reconstruction. The field experiments were conducted in Xi’an, China. The results show that the proposed framework based on the AC-UAV system is capable of conducting mobile complete traffic data analysis tasks and also feasible for large-scale automated city applications.

In this paper, ANSYS finite element software was used to establish a three-dimensional finite element model of pavement structure. Dynamic load was applied to study the influence of axle load and vehicle speed on mechanical indexes of asphalt pavement structure. The results show that the mechanical indexes such as shear stress, tensile strain, tensile stress of the base layer, and compressive strain on the top of the subgrade of the basalt fiber asphalt pavement structure are improved to different degrees than ordinary asphalt pavement. Under dynamic load, the compressive strain on the top of subgrade and shear stress of the basalt fiber asphalt pavement at 120 km/h are reduced by 8.4% and 12.12% respectively compared with 60 km/h; When the axle load is 100 KN, the maximum shear stress of surface layer, the tensile stress at the bottom of the subbase layer and the compressive strain on the top of the subgrade are reduced by 2.54%, 2.66%, and 2.56%, respectively.

The effect of basalt fibers on fiber adsorption property, cracking resistance, and shear resistance of basalt fiber asphalt mastic was studied by a self-designed adsorption test, tensile test and shear test. From the results, the optimal basalt fiber content was recommended. At the same time, the rheological properties of basalt fiber asphalt mastic under optimum fiber content were studied, and the microstructure of fiber mastic was observed by scanning electron microscopy. The results showed that adsorption capacity of basalt fiber enhances with the increase of fiber contents, while the shear and tensile strength of fiber mastic presents a “rising-dropping” trend with the increase of fiber contents. The optimal content of basalt fiber is 7% based on comprehensive properties. The interface between basalt fiber and asphalt is well bonded, and the comprehensive properties of asphalt mastic are improved by reinforcement and bridging of basalt fiber.

To describe the test and evaluation capability of intelligent vehicle infrastructure system (IVIS) systematically, this paper proposes a new intelligent vehicle-road system evaluation method to improve effective coordination of end-to-end cloud architecture. Using a single intersection environment for system integration test, the test index consisting of efficiency, safety, environmental protection, resources, and comfort was obtained. The AHP-entropy method is used to combine and assign weights to IVIS indexes to determine the weight of each index. The evaluation value of IVIS and ordinary roads is calculated to show the running status of the evaluation object. Vissim simulation software is used to build IVIS simulation platform to evaluate and verify the performance and functions of IVIS and ordinary roads at a single intersection by comparing operation conditions. Results show the IVIS system can effectively improve the efficiency of the traffic system within the allowable range of the index set of IVIS system.

To fill the research gap of highway interchange design standardization, this paper puts forward the definition and main research ideas of interchange design standardization after analyzing the design standardization of buildings and bridges. Based on the analysis of 94 A type single trumpet interchange design data, this paper determines its standardized model and corresponding standardized parameters, then uses genetic algorithm to optimize the values of some standardized parameters. Finally the standardized recommendation value of the A type single trumpet interchange of the expressway in the plain area was determined and the using method of the recommended value was given. Case analysis shows that it can improve driving safety and reduce the repetitive design of traffic sign, bridge, and culvert layout. Realization of the interchange design standardization lays the theoretical foundation for parametric design, factory production, assembly construction of interchange components as well effective combination with BIM technology in interchange area.

Some bridges are in deep water, the dynamic response of the structure is affected by the long-term interaction between deep water and waves. At present, most of the research studies focus on deep-water bridges with the solid piers. However, there are few studies on the fluid-structure interaction of deep-water bridges with the hollow piers under the effects of an earthquake, flowing water and waves. In this paper, the hollow pier is used as the research object to analyze the dynamic response under current action, wave action, current action, and wave action. The results show that the dynamic response of the pier is not the direct superposition of the single action of the current and the wave.

In order to analyze the seismic earth pressure of cohesive soil retaining wall, the general method and simplified method for calculating seismic earth pressure are established in this study through Rankine’s earth pressure theory and Coulomb earth pressure theory. The two methods are analyzed and compared, and the following conclusion is drawn. Using the simplified method, the “internal structural pressure” is added around the retaining wall. Assuming that the cohesion between the filling and the wall is the same, the cohesive soil can be transformed into cohesionless soil to analyze. Using the general method, cracks will occur at the bottom of the wall, that is, the 0-point position of the earth pressure intensity is a distance from the heel of the wall. The distribution patterns of seismic earth pressure intensity calculated by the two methods are quite different, but the overturning moment at the bottom of the wall is less different.

Non-stick wheel emulsified asphalt was developed by using emulsifier, reinforcing and toughening agent, elastomer modifier, and base asphalt; and used to prevent construction vehicles from stripping the sticky layer of oil which causes bad interlayer adhesion. The basic properties, non-stick wheel properties, bonding properties of self-made non-stick wheel emulsified asphalt, SBS modified emulsified asphalt, and commercial emulsified asphalt were studied. The results show that the softening point and ductility of the asphalt are much better than those of the non-stick wheel emulsified asphalt. The interlaminar bond strength at 5°C, 25°C, and 40°C is higher than that of commercial non-stick wheel emulsified asphalt and SBS modified emulsified asphalt; this shows that it has excellent non-stick wheel characteristics. Finally, Reflectance test and infrared spectroscopy were used to study the micro-mechanism of self-made non-stick wheel emulsified asphalt.

This paper studied the performance of high ash rock bitumen (BRA) modified asphalt mixture. To investigate the change of the properties of asphalt mixture subjected to the modification of BRA, test on the pavement performance of the BRA modified asphalt mixture with different BRA substitution ratio under the condition of powder to rubber ratio 1.2 were carried out. The test results and analysis indicate that with the increase of the BRA replacement ratio, the high temperature performance and water stability of the mixture are significantly improved, and the low temperature crack resistance performance increases slightly. The optimal replacement ratio of BRA is 75%. The mechanism of BRA modification was analyzed by scanning electron microscopy (SEM) and fluorescence microscopy. The results showed that BRA can modify the interface of asphalt cement, improve the thermal storage stability of asphalt cement, and increase the adhesion between asphalt cement and aggregate.

Aiming at the unsatisfactory effect of repairing semi-rigid base cracks by existing polymer grouting materials, a polyurethane grouting material with excellent repairing and reinforcing effect was developed. Through the indoor test, the effect of repairing the water-stable base crack of the material was evaluated. The results show that the grouting material developed in this paper is better than conventional polymer materials in repairing and strengthening the cracking water stability. Based on FTIR analysis, the mechanism of repairing and strengthening cracks in water-stable base by polyurethane grouting material was proposed.

Given the difference in body size and dynamic performance between passenger cars and trucks, they have inconsistent road demands. The radius of horizontal curve of the truck-only interchange ramp (TOIR) is one of the key indicators affecting the steering safety of the truck. In this paper, the six-axis articulated truck with a full load (SATFS) is selected as the designed vehicle. Combined with its dynamic performance, by analyzing the body tilt rate of SATFS and the elastic deformation caused by the suspension device, a vehicle steering mechanics model based on rigid-flexible coupling effect is established. The minimum radius of horizontal curve of the TOIR in the general area is respectively proposed. Finally, the rationality of the proposed value of the minimum radius of horizontal curve of TOIR is verified by Trucksim software simulation experiment.

Crack and frost resistance are factors affecting performance of road concrete. In order to compare the effects of mineral admixtures such as fly ash, silica fume, and slag powder on the plastic cracking and frost resistance of concrete; the cracking time, number and area of cracks, Cl⁻ diffusion coefficient, and relative dynamic modulus of concrete was determined by various methods. The results show mineral admixture can accelerate or delay the occurrence time of cracks. The amount is positively correlated with the average cracking area, but there is no significant correlation with the number of cracks. Mineral admixtures can effectively reduce Cl⁻ diffusion coefficient. However, after exceeding 20%, the rate of decrease slows. The effect of mineral admixtures on improving the frost resistance durability of concrete has become more apparent in the late stage of freezing and thawing. With the appropriate dosage, the three admixtures can achieve similar effects.

The study of the traffic accident mechanisms on the spacing-restricted exit, as well as the exploration on the law and trends of traffic accidents in the existing traffic conditions, can effectively prevent the traffic accidents in the area of small interchange spacing, which is important to improve the security of such area. By analyzing specific cases, this paper makes clear the types of accidents, and then elaborates the relationship between traffic accidents and the traffic flow parameter such as traffic volume, velocity, traffic composition in the area of small interchange spacing. For the high number of rear-ending collision cases, it researches rear-end collision mechanisms on such an area. It provides a theoretical basis of the establishment of the traffic accident model and the research of the security design technology of the interchange spacing-restricted exit.

In order to understand the relationship between tunnel vertical alignment and traffic safety, based on the statistics and analysis of data of ten expressways and their adjacent sections in Zhejiang Province for 6 years, the influence mechanism of the vertical alignment on the tunnel traffic safety is obtained by analyzing the vehicle speed dispersion, braking distance, and accident rate of the tunnel entrance and exit sections. Finally, according to the speed prediction model, the influence mechanism of the vertical alignment of the tunnel entrance section on traffic safety is obtained. The results show that the larger the longitudinal slope in the tunnel entrance and exit area, the higher the accident rate. The accident rate is lower when the longitudinal slope of the tunnel entrance area is in the [−1.0%, 1.5%] interval and the longitudinal slope of the exit area is in the [−1.5%, 1.0%] interval; the tunnel entrance accident rate is positively correlated with the initial speed of the vehicle before entering the tunnel entrance area. The accident rate of the tunnel entrance increases with the increase of the average slope before the tunnel.

Intersections in rapid urbanization areas are the place of high accident rate and traffic congestion. Research on the form and applicability of intersections of different traffic function and management according to traffic characteristics and the restriction of land resources conditions is of great importance for improving intersection safety and capacity. This paper analyzes the design factors of intersections and the influence of traffic channelization and control measures on the traffic capacity of intersections, and puts forward the optimization design method of intersections based on service level adaptability of intersections. This provides support for the form selection and optimization design of intersections.

The continuous expansion of expressway network scale and increase of electromechanical system faults put forward higher requirements for spare parts inventory demand forecast. The lack of maintenance data becomes one of the major challenges for factor analysis. This work presents a forecast method of spare parts demand for expressway electromechanical equipment. The approach first utilizes a segmented fault model to characterize fault characteristics of equipment. Then, the failure rate, which is transformed by the running state of equipment using the above fault model, combined with other related factors is used to construct an influence factor sequence. Finally, the factor sequence is fed into the improved random forests model to predict the demand. Experiments performed on an automatic railing machine of the charging system indicate that the failure rate plays an important role in the demand forecast. The proposed method shows significant performance gains when compared with the current state-of-art methods.

In order to increase average speed on expressways, restrain the phenomenon of overspeed and sudden change in speed, and improve public satisfaction, it is necessary to study the effects of an adjustment in sign speed. This paper evaluates the expressway sign speed adjustment project by focusing on operating speed and analyzes the coordination of operating speed and sign speed. The relationship between the accident rate per million kilometers of highway in Zhejiang Province and the vehicle speed coefficient of variation is established, and the vehicle speed coefficient of variation is broken down and evaluated. A reasonable speed is proposed to support law enforcement and compliance; the research into speed is summarized to ensure safe and efficient driving.

In vehicle accident type analysis, when negotiating a curve, skidding, and rollover are determined to evaluate vehicle lateral stability. For front-wheel drive passenger cars (SUVs) with a high center of gravity, CarSim was used to establish a human-vehicle-road simulation model. The most unfavorable road geometric combination models for horizontal curve, grade, and superelevation, with design speed of 100 km/h was established from the “technical standard of highway engineering” (JTG B01-2014). The influence of above road models under good (0.80) and wet (0.50) asphalt pavement conditions on the lateral stability of passenger cars while negotiating steady state cornering was studied. The accuracy of the models is verified by comparing the skidding safety margin of the CarSim model and the mass point model from the standard. This study is useful for future road geometric design and transportation safety design, and further enriches and improves the theory and method of road alignment design.

To reduce misdriving and traffic delays at interchange exits, transportation efficiency, and safety at these interchanges must improve. After studying interchange exit safety, principles of traffic engineering and ergonomics are applied, and theoretical calculation and comparative analysis are used to study the influence of mandatory lane signs and navigation systems on behavior at interchange exits. Analyzing the function of and relationship between mandatory lane signs and information service characteristics of car navigation systems, the distribution method of driver visual resources is studied according to probability and statistics theory, and then an information coupling model based on mandatory lane sign and navigation system function is established. From the perspective of lane indication concept and according to the driver’s cognitive behavior, interchange exit behavior is analyzed, and the adaptability requirement of the exit form based on lane indication and navigation coupling function, as well as the interchange exit optimization form, is proposed.

By analyzing a driver’s nighttime visual characteristics, combined with the vehicle body contour, the headlight scattering angle, and other limiting factors, the driver’s night driving field of view is obtained. Based on the relationship between the driver’s night vision field and the road plane line shape, the contour making calculation model of the highway is established. Using UC-win/Road simulation software, simulation driving platform, and eye tracker, the simulated driving under different sight guidance facilities setting schemes is carried out, and the visual indices and vehicle running indices of the testers under different setting schemes are compared. Through this, we can verify the rationality of the setting spacing of the contour marking. The research results will help to set the contour marking of different types of road sections on the expressway and remind the driver of the linear change of the road to ensure greater safety while driving.

This paper presents the nonlinear finite element analysis of hollow slab beam strengthened with steel plate-fiber polyurethane cement (SP-FPUC) composite. Comparing with single reinforcement, the innovative strengthening technique is found to improve the carrying capacity of hollow slab beam and the strain of steel bar and concrete is significantly lower under the same load. The model of a hollow slab beam bridge with damaged hinge joints on Harbin-Daqing Freeway was established to analyze the effect of composite reinforcement on the lateral transfer performance. The results indicate that the lateral distribution factors curve approaches linearity with increasing FPUC thickness and the integrity of the hollow slab beam is well improved. The stress of FPUC laminate is greatly affected by the different deflections of adjacent slabs and the stress of steel plate is related to FPUC thickness and beam deflection. This indicates that SP-FPUC has strengthening potential for deteriorated hollow slab beams.

In recent years, China’s bridge aesthetics have become valued and famous for their elegant modelling and for the lightweight structure of the super-wide bilateral steel box girder self-anchored suspension bridge, which is widely used in urban bridge construction. Using Yunlongwan Bridge in Chengdu, Sichuan province as the research object and the finite element analysis software ANSYS to establish a finite element beam and shell element model, this paper analyzes the super-wide bilateral steel box girder’s transverse stress and transverse deformation. Under the influence of shear lag effect, the local effect of the girder roof is obvious. In addition, negative shear lag effect occurs at the junction of the transverse and longitudinal girders, and the girder’s torsional deformation is obvious under an unbalanced load.

In order to improve the long-term performance of pavement structure, from the point of view of increasing the modulus of the structural layer, the influence of the modulus of the medium layer on the long-term performance of pavement structure was analyzed through mechanical simulation. Then the structural reinforcement scheme of pavement was drawn up and compared with the original pavement structure scheme. Furthermore, combining with the physical engineering, the feasibility of the scheme was demonstrated. Finally, the results show that, by increasing the modulus of the mid-surface layer, the anti-rutting, and anti-fatigue performance of pavement structure was improved. And through the post-construction test, the pavement performance of the test road, which was implemented by structural reinforcement technology, was proved to be in good condition. Also, the scheme of improving the long-term performance of pavement structure by increasing the modulus of the mid-surface layer was confirmed to be feasible.

The performance prediction of asphalt pavement is an important part of pavement maintenance decision-making. First, the key factors affecting the pavement performance attenuation was selected. Next, the model of performance prediction and the law of performance development were studied with the reasonable performance evaluation index based on the combination prediction principle. Finally, the results show that the combined forecasting model based on Grey Theory and regression analysis method was confirmed to be feasible. This could provide powerful guidance and help for the determination of maintenance time of expressway asphalt pavement.

Filling unprocessed soil containing a large amount of construction waste, industrial waste, or household waste, when the road route must pass through the area, will seriously affect the construction of roadways. In this paper, the mechanical properties of the different reinforcement methods of the miscellaneous soil are studied, and the intensity and deformation change law of the reinforcement of the miscellaneous soil are studied by the experimental index of the pressure strength, cracking strength, rebound ingesting modulus, CBR value, and organic matter content. Through an indoor test, the comprehensive strength, carrying capacity, anti-deformation ability, and economic index of the reinforced landfill were compared with the analysis, and the experiment proved that the reinforcement effect of 0.08% content curing agent was the best. The results can effectively reduce the harm caused by road landfill to the safety of highway foundation and improve the availability of highway landfill.

Super-wide steel box girders are widely used in the construction of long-span bridges because of its light weight and attractiveness. To accurately understand the influence of the cross diaphragm and suspender on the shear lag effect of the self-anchored suspension bridge, we used the ANSYS finite element analysis software to establish a multi-scale structure model and refined analysis of the Cheng Du Yun Long Wan Bridge. We examined the influence of diaphragm and suspender on shear lag effect of steel box girder under different loads. The results show that: under different loads, the stress of the roof at sections with a cross diaphragm is less than those without a diaphragm, so setting the diaphragm is beneficial for the bridge deck. The stress of the roof with a suspender is less than those without a suspender. Lastly, the thickness, number, and distance of diaphragms have a significant effect on shear lag.

Damage of cement concrete pavement brings great pressure to the highway management and maintenance departments. In-situ granulation technology is a technology that uses the rotary split crushing method to crush the preliminary pre-broken cement concrete slab pavement on the original pavement to form a designed grading aggregate, and then adds a certain amount of stabilizer to the site to form the mixture, which becomes the water stable base after shaping, rolling, and curing. In this paper, the recycling of in-situ granulated stone in the test road is completed by using the rotary in-situ granulated stone technology relying on real engineering, and the best crushing method and cement dosage, determining indicators. After the post construction inspection, all indicators of the water stable base of in-situ granulated stone recycling meet the requirements of the design specifications, which show that in-situ granulated stone technology can be applied to the maintenance of rural roads.

To solve the problems of construction quality decline caused by leakage, under-pressure, over-pressure, low construction efficiency, and strong vibration environments more research is needed to reduce the body comfort of operators in roadbed earth-rock rolling construction. Based on high precision satellite positioning and intelligent sensing technology, the unmanned roller is developed, and it is applied in practical engineering. Finally, the unmanned precise driving of the roller is realized. It provides practical experience for the intelligent construction of subgrade with unmanned machinery.

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid, and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

In the construction of asphalt mixture pavement, there are many paving construction procedures, high requirements for machinery and personnel, unreasonable technology, and so on. In this paper, the millimeter-scale 3D automatic paving control technology of asphalt mixture pavement is studied with an engineering example. From the analysis of the test results in the later stage of construction, the technology has achieved the goal of high precision paving, and the successful application of the technology provides valuable reference experience for similar asphalt concrete pavement construction in the future.

Aiming at the traffic safety and congestion problems caused by rapid urbanization, based on the traffic safety and operational efficiency of highway, the minimum standards for highway entrances and exits spacing were studied. According to the typical design parameters, such as highway function and technical grade, number of lanes, design speed, design service level, design capacity and minimum operating speed, the types of highway entrances and exits and spacing in rapid urbanization areas were classified. Based on the analysis of the influencing factors and influencing ranges of highway entrances, and exits spacing in rapid urbanization areas, the access spacing model of highway entrances and exits on the same side and on both sides were established, and the minimum spacing standards between entrances and exits based on traffic safety and operational efficiency were proposed. The research results can provide effective theoretical and technical guidance for the construction and management of highway entrances and exits in rapid urbanization areas. It is of great significance to improve the safety and capacity of highway entrances and exits and reduce travel delays.

The ultra-wide double-sided steel box girder self-anchored suspension bridge can be better used in urban bridge construction due to its delicate linear shape and light structure, as well as not being restricted by geological conditions. This study focuses on the Yunlongwan Bridge in Chengdu, Sichuan Province, and the ultra-wide separated steel box girder. We used ANSYS finite element analysis software to establish the beam-shell element combination model to study the lateral deformation and stress at the mid-span target section under eccentric loads. The stress amplitude of the roof and its distribution and diffusion along the transverse direction under different stiffness of the bridge are analyzed. Finally, the influence of the tensile stress and stress amplitude of the bridge deck on the fatigue of the bridge structure and the durability of the bridge deck pavement is discussed.

We established a novel speed prediction model incorporated with changing slope on vertical curve. The speed prediction model was proposed by using higher mathematical calculus and iteration method, considering the variable slope on the vertical curve. The speed-distance curves of trucks on the continuous long uphill section was obtained by inputting the weight-to-power ratio of trucks and vertical alignment indexes in VB (visual basic) programming. The results show that the novel speed model is totally different from that of original speed prediction model (constant vertical slope) on vertical curve sections. Specifically, the speed deviation of the two models reaches the maximum at the gradient change point of slope, and it increases with larger power ratios, larger slope indexes, or higher altitude. The proposed speed prediction model can provide an accurate reference for grade length determination and climbing lane design, increasing driving safety and traffic efficiency.

With the development of the economy, some freeways have been overloaded by vehicles and need to be reconstructed or expanded. In addition, some freeways have been damaged and maintenance work is required. In order to ensure safety on the freeway during the construction period, alleviate traffic jams in work zones, and improve the level of service, it is necessary to carry out a traffic organization scheme design of the work zone. This paper first analyzes the traffic characteristics of the work zone, then studies the traffic organization of the work zone from three aspects: section division, traffic facilities setting, and speed limit. Finally, this paper puts forward the traffic organization scheme in the work zone, which solves the problems of vehicles frequently changing lanes and the number of available lanes being reduced. This scheme is conducive to ensuring traffic safety and improving traffic capacity in the work zone.

Pavement condition index (PCI) is a comprehensive indicator describing the overall damage of pavement surface and can be used to determine the pavement maintenance needs. However, due to its combination of multiple distress, the same PCI score may represent different combinations of pavement distress, making it impossible to make accurate preservation decisions. Therefore, it is necessary to conduct a more detailed grouping study on pavement distress data and establish a relationship between the refined data packets and PCI. Based on the actual distress data of Shanghai urban asphalt pavement, this study firstly uses the sequential clustering method to group the pavement segment data according to the PCI score. Then the distress characteristics of each group were analyzed and the differences between different damages were quantitatively calculated. The results show that the impact of the pavement distress combination on PCI is not uniform and the PCI score cannot fully reflect the complexity of pavement distress. When the PCI level is moderate, the variability of the pavement distress combination is the largest.

In order to realize intelligent event management of freeways and improve its safety and efficiency, this paper presents research on traffic information monitoring system to cooperative vehicle instructure system (CVIS). The current traffic information monitoring system monitoring data content and its characteristics are described first. Then, the principle of traffic information data collection and exchange is analyzed to guide the routine collection and exchange of data. Meanwhile, the traffic information monitoring scheme and traffic information monitoring system architecture are established according to the content of traffic information monitoring and the principle of collection and exchange. After that, the existing problems and technical requirements of traffic information monitoring system to CVIS in China are analyzed. Finally, according to the technical requirements, the paper puts forward the improvement of the traffic information monitoring system.

In order to study the regularity of road surface roughness, based on summarizing and analyzing the domestic and foreign road surface roughness prediction models, the representative AASHTO empirical model is selected as the prototype of road surface roughness prediction, and the rutting standard deviation prediction method is established The existing model is improved, and the typical pavement structure is tested by secondary compaction, PQI detection and IRI prediction, and the pavement roughness is predicted. The results show that the quantity and compression ratio of the mixture with different initial voids can be detected by secondary compaction test, and the unevenness of voids can be detected by PQI and rapid compaction nondestructive testing instrument. Therefore, the improvement of the existing prediction model can make the prediction results more objective. And by using the second compression formula and the air voids measured in the field, the different compression amounts of each point can be calculated, and the standard deviation of the compression amounts can be taken as the standard deviation of the ruts; the ruts caused by the second compression can account for 7.4%∼17% of the total ruts It is necessary to pay attention to the role of secondary compaction in rut control and prediction.

To deal with the lack of high temperature performance and adhesion performance of SBR modified emulsified asphalt used for preventive maintenance seal layer, WER/SBR composite modified emulsified asphalt is prepared by adding waterborne epoxy resin (WER) and curing agent with different admixture amounts into SBR modified emulsified asphalt. The formation mechanism of composite modified asphalt is analyzed by fluorescence microscopy and Fourier infrared spectrum. The adhesion between composite modified asphalt and aggregate is evaluated by adhesion test. Results show that the incorporation of waterborne epoxy resin causes physical and chemical changes of SBR modified emulsified asphalt and the phase structure of changes when the admixture amounts reaches 40%. At the same temperature, the increase of water-borne epoxy resin content improves the bond behavior of composite modified asphalt. The suggested percentage of admixture of waterborne epoxy resin in SBR modified emulsified asphalt is about 40%.

Composite nano-TiO₂/CaCO₃ was prepared by coating a layer of nano-titanium dioxide on the surface of calcium carbonate as asphalt modifier. Results show that the effect of technological conditions on the properties of composite nano-TiO₂/CaCO₃ modified asphalt is as follows: reaction temperature > shear rate > shear time. The optimum content of composite nano-TiO₂/CaCO₃ is 5% and its preparation process is that the reaction temperature 170°C, the shear rate 5,000 r/min, and the shear time 40 min. The addition of composite nano-TiO₂/CaCO₃ changed the viscoelastic ratio of asphalt, significantly improved its high temperature stability and anti-aging performance. Although its low-temperature stress relaxation ability is reduced, it still has good application value. Meanwhile, the viscosity temperature index (VTS) indicates that nano-TiO₂/CaCO₃ reduces the temperature sensitivity of the asphalt and has good construction performance.

In view of current research on the microscopic properties of SBS modified asphalt under high temperature and rainy environment, the microscopic moisture-thermal damage behaviors of SBS modified asphalt were studied by atomic force microscopy and Fourier transform infrared spectroscopy. Two indexes, namely, the surface roughness and the percentage of bee structures, were introduced to quantify changes in the microstructures of SBS modified asphalt. Increasing the immersion temperature not only accelerated the evolution of morphological structure, but also significantly affected the mechanical behavior of micro-phases. To evaluate the influence of moisture-thermal conditions on composite phase properties, a parallel model was applied. It was found that adhesive force of bee structures was the main contribution to the surface macro adhesion. Finally, FTIR tests confirmed that the formation of nano-bumps was closely related to sulfoxide index, which indicated that polar functional group was the mainly origin of nano-bumps.

The visibility between any two points in the highway can be judged by analysis of the 3D line of sight, which is of great significance for the inspection of the 3D highway line of sight and the determination of the sight triangle of the intersection. The paper focuses on the inspection methods and visibility analysis of highway 3D line of sight based on the principle of the line of sight analysis and the Civil 3D platform. Based on the review and evaluation of the sight distance inspection module of Civil3D platform, a reasonable definition of a three-dimensional line of sight was proposed, and the pasting algorithm of surfaces was designed. Three 3D sight distance calculation algorithms were compared and analyzed, then a fixed step scanning algorithm for sight distance inspection was designed.

An evaluation system which comprehensively considers the multi-factors of driver, vehicle, and highway elements, can objectively reflect the operation state of the road. The paper established a simulation system based on comprehensive consideration of driver-vehicle-highway system (DVH). The true operation status of vehicle was simulated using vehicle dynamics software ADAMS (automatic dynamic analysis of mechanical systems). By reverse study to the sensitive indexes, highway geometric design parameters could be safely optimized. The purpose of simulation was to find out the sensitive indexes of vehicle dynamics parameters that affected driving safety. A simulation case showed that six dynamics indexes were sensitive to the changes of curve radius, including lateral acceleration, roll angle, steering wheel torque, and steering wheel angular velocity, yaw rate, and sideway force coefficient. Finally, highway geometric design can be achieved by calibrating thresholds of dynamics indexes and safety evaluation.

In order to improve the safety and traffic capacity of the intersection, and reduce the delay of the intersection, this paper studies the safety adaptability of the intersection according to traffic flow characteristics and constraints of land resources at intersections in quickly urbanized areas. A set of relatively complete indicators for the evaluation of the safety adaptability of highway intersections in rapid urbanization areas is proposed, and the weights of each indicators are determined. Combined with the engineering example of the intersection in the rapid urbanization area of Ningbo, Zhejiang Province, the safety adaptability evaluation microscopic model of highway intersection is established by using the mathematical methods such as analytic hierarchy process, dynamic weight function, and membership function. We also provide theoretical and methodological support for traffic safety adaptability evaluation of intersections.

In order to explore drivers’ stopping sight distance needs, ensure the drivers’ driving safety, and consider the project cost, accident types on median strip of freeway without sufficient sight distance in Zhejiang province is analyzed and classified. Meanwhile, the car-following model and the lane-changing model are proposed based on the stopping sight distance model. The lengths of sight distance and corresponding curve radius required for vehicle safely to follow and change lanes are computed. The results show that the driver’s demand for driving safety sight distance will be significantly lower than the stopping sight distance required by “design specification for highway alignment.”

Cement content affects the performance of cement stabilized macadam base, particularly affecting its strength, and durability when the gradation is certain. To explore the influence of cement content on the performance of cement stabilized macadam base in the seasonal frozen zone, 4%–8% (1% interval) of cement were selected respectively and tested by unconfined compressive strength test, dry shrinkage test, temperature shrinkage test, and freeze-thaw cycle test based on use requirements and climatic conditions of the seasonal freezing zone. Results show that the unconfined compressive strength of cement-stabilized macadam increases gradually with the increase of cement content, and the strength increases greatly at 6% cement dosage. The dry shrinkage strain of cement stabilized macadam under different cement contents continues to increase with time. For strength and durability, 5%∼6% is the optimum cement content for the test gradation cement stabilized macadam in the seasonal freezing zone.

With the gradual improvement of highway networks in China, it is necessary to study the maintenance sequence of network-level pavement, that is to invest limited maintenance funds in the highway sections where maintenance is most in need to maximize benefits. In this paper, the core index of network pavement maintenance sequence is considered comprehensively, and the index system is established. Then, the weight of each index is determined by using the reciprocal matrix based on preference information. Finally, a complex element extension method is used to establish a matter-element analysis model to analyze the asphalt pavement maintenance sequence of highway networks. The results show that the study of network pavement maintenance sequence can be used to guide the formulation of pavement maintenance planning, and it is of great significance to network pavement maintenance management and scientific decision-making in China.

Taking the curved tower and curved girder cable-stayed bridge without back stays of Dongguan Waterway Bridge as the engineering background, the characteristics of three kinds of closure methods, i.e., matching cutting method, incremental launching method, and combined method, are analyzed. Combined with the structural characteristics of the bridge and the construction site temperature and other natural conditions, the matching cutting method is selected. By means of real bridge test and finite element simulation, the key construction parameters are calculated, and analyzed. The construction steps, key closure technology, and actual construction situation are introduced in detail. Practice proves that through effective closure construction control, the matching cutting method is quick in construction, accurate in matching and smooth in closure.

This paper introduces three typical structural forms of permeable asphalt pavement in detail and systematically analyzes the functional positioning and applicable scope of each typical permeable asphalt pavement structure. Twenty typical cities were selected for the analysis of urban rainstorm intensity in Guizhou Province and regional division according to the characteristics of urban rain patterns. By studying the peak rainfall intensity and total precipitation in typical cities, it is found that the characteristics of urban rainfall in Guizhou Province are generally “spindle-shaped”, that is, the intermediate ratio between the two is large and the proportion is small. The change of rainfall intensity and total rainfall near the peak of rain intensity for 30 min was selected as an important parameter to analyze the characteristics of urban rainstorm. According to the distribution of average rainfall intensity, the urban rain pattern in Guizhou Province was divided into four regions.

Top-down cracking is a typical type of damage to asphalt pavement that significantly impacts the traffic safety and reduces the pavement’s service life. To understand the mechanism and the regularity of the crack propagation in asphalt layers, the dynamic responds of an asphalt pavement containing a top-down crack were derived. An asphalt pavement containing a top-down crack was modeled as an elastic multi-layered system. The Fourier and Laplace transform and inverse transform were introduced to transform the partial differential equations to ordinary differential equations for solving the governing equations. Lobatto–Chebyshev integration formula, as a numerical method, was used to gain the numerical results of the singular integral equation in Laplace domain. Numerical analyses were conducted to illustrate the influences on the crack propagation in the asphalt pavement. The procedure developed in this study provides an effective tool for asphalt pavement performance evaluation and finding effective maintenance measures for cracked asphalt pavement.

Steel slag, as a kind of industrial waste slag, has good road performance, and can be used to prepare asphalt mixture instead of stone. Therefore, the study of adhesion between asphalt and aggregate interface is of great significance for the design and selection of steel slag asphalt mixture. Based on the theory of surface free energy, the work of adhesion was calculated on the asphalt and steel slag in the dry and wet state, the steel slag—asphalt system adhesion ability was evaluated quantitatively. The surface microscopic characteristics of diabase and the steel slag was analyzed in detail with the aid of scanning electron microscopy (SEM), the result showed that a lot of cross section and distribution of the surface uplift, fuzzy more pore is the key of the steel slag wrapped with more asphalt.

Nowadays the importance of traffic safety issues has become increasingly prominent. The intersection in road traffic safety is one of the key points. The displaced left-turn intersection is a new type of plane intersection proposed to effectively reduce the conflict at the intersection of planes and improve the traffic capacity. However, at present, China has only built a displaced left-turn intersection in Shenzhen. This paper is mainly about the traffic organization of the displaced left-turn intersection and comparing it with the traditional cross-shaped plane. It then simulates the traffic at the intersection of planes in various traffic environments (including free flow, slightly disturbed traffic, and large traffic congestion). Using the comparison of vehicle delay, number of stops, driving time and other indicators to propose a situation suitable for the construction of the left-turn drift lane plane crossing. Finally, the Vissim system simulation model is used.

Highway bridges are an important part of modern traffic construction, and their maintenance and preservation are critical. Bearing is directly related to the stress state of the overall structure and the overall traffic safety. However, at present, the detection of bridge bearing is usually carried out manually, which not only consumes enormous manpower and material resources, but also affects the normal traffic operation, and ensuring inspectors’ safety is difficult. Through the rational use of advanced science and technology, and under the guidance of in-depth learning and image processing technology, this research carries out software development to detect and identify bridge bearing problems. The application and experiment in a specific engineering case show that the detection and recognition method based on convolution neural network has strong intuition, and can realize the prediction of multiple images under the same folder, and describe the deterioration situation in detail.

Due to the harsh cold weather conditions in the heavy frozen areas and the increasing number of overloaded vehicles, asphalt pavement distresses occur frequently in the early service period, among which the crack distress is the most prominent, seriously affecting the driving safety and the service life of the pavement. In order to systematically analyze the change law of mechanical response of basalt fiber (BF) asphalt pavement and its crack resistance behavior, the temperature field model under extreme low temperature conditions was established and the mechanical response of pavement structure under the adverse conditions of low temperature-load coupling was calculated and analyzed. Through comparison of the crack resistance mechanics control index, the crack resistance effect of BF asphalt pavement under low temperature-load coupling condition was analyzed. The results show that under the adverse conditions of low temperature-load coupling, the crack resistance mechanical indexes of BF asphalt pavement have improved to different degrees compared with ordinary asphalt pavement. Specifically, the maximum tensile strain at the bottom of the asphalt layer, the maximum shear stress of the asphalt layer, and the maximum tensile stress at the bottom the base layer in the BF asphalt pavement are decreased by 5.5%, 5.51%, and 20.03%, respectively, compared with the ordinary asphalt pavement.

Best timing of highway asphalt pavement preventive maintenance was investigated in the study, considering the pavement overall quality, the cost and the benefit. The optimization is to maximize the overall ratio of benefit over cost in the analysis period through the method of integer programming. A case study on the highway asphalt preventive maintenance decision-making in Jiangsu Province was conducted to illustrate the proposed procedure. The case study clearly demonstrated the applicability and rationality of the optimization model based on benefit-cost analysis. The results can be used as a guideline for highway agencies in their pavement maintenance decision-making process.

In order to ensure the driving safety of vehicles on dedicated ramps of expressway with separated line between passenger and freight, and to enrich the research results on the length of the acceleration lane of dedicated ramp for truck, this paper analyzes the effects of various factors such as traffic volume, headway distance, vehicle’s running speed of adjacent lanes in the merging-area, acceleration performance, and driver’s quality on the length of acceleration lane. At the same time, the UMRR Chain Kepler Radar Speedometer is used to observe the actual data in the interchange. The mathematical statistics method is used to study the driving characteristics of the truck flow in the merging area, and finally, the acceleration lane is divided into a stepped-up segment, waiting segment, and transition section. From the perspective of kinematics theory and traffic flow theory, this paper establishes a calculation model for the length of truck’s acceleration lane of expressway of the separated line between passenger and freight transportation. Finally, the paper puts forward the characteristic parameters of the truck in the calculation model for the acceleration lane’s length by observing the actual running characteristics of the truck in the merging-area.

In order to solve the problem of arbitrariness and qualitative analysis in selecting wavelet function, a quantitative analysis index for modal parameters identification was proposed from the view of correlation, energy, and entropy. The continuous wavelet transform of vibration response signal for different wavelet functions was conducted. The correlation coefficients between the original signal and wavelet transform coefficients was computed, and then the preliminary selection of wavelet function with large correlation coefficient was finished. Then, the index of the ratio of energy to entropy was introduced based on the criterion of energy and entropy. Finally, a numerical simulation of a system with three degrees of freedom was used to verify the proposed index, and the research result shows that the optimal wavelet function for modal parameters identification is the complex morlet function. The wavelet function selected is used to identify the modal parameters of Z24 bridge and the suspension bridge.

A novel scheme combining image processing with deep learning is proposed to solve the problems of illumination non-uniformity and impurities in asphalt pavement images. The method first used illumination non-uniformity correction, contrast enhancement, and image denoising methods to highlight the cracks. The SegNet network is then used to achieve effective crack segmentation. Finally, through morphological methods and regional connections, interference is effectively removed, and the final crack skeleton is obtained. The results show that the Mean Intersection over union can reach 70.08%, which is about 3% points higher than that of the method without pre-processing, and achieves a good detection effect.

A lot of resources are required to construct autonomous vehicles (AVs) proving ground, so it is necessary to design proving ground with the goal of improving its testing ability. In this paper, a mathematical optimization model of road horizontal alignment design based on genetic algorithm (GA) for continuous loop area in AVs proving ground is proposed, the test case coverage is used as an index to measure the testing capability of it. This method takes continuous loop area boundary and test cases of autonomous vehicles as input, and generates road horizontal alignment design of continuous loop area automatically. Finally, the method is validated by an example of practical AVs proving ground. The result shows that compared with the design based on subjective experience, the test cases coverage of the design obtained by this method is increased by 9.4% and 6.7% under the two different test cases databases respectively.

With the development of intelligent transportation in China, improving the safety and efficiency of winter road operation through the realization of intelligent ice condensation warning on the pavement has become the focus of current traffic engineering. This paper considers the impact of winter snow and ice disasters and road ice condensation on traffic operation, proposes the construction of an intelligent road ice early warning system, designs the overall implementation plan, and requirements and architecture of the system. Taking the winter Olympics transportation roads as an example, an intelligent road ice early warning system was developed through vb.net and SQL Server databases. The results show that the intelligent road ice early warning system can timely warn and classify the road ice condensation status, ensure the safe operation of the road, and have application value for improving road safety and operating efficiency.

The role of carrier in anti-freeze material is studied through a physical adsorption test to confirm the optimum salted carrier. The influence of carrier on slow-release property of anti-freeze material and salt-releasing in asphalt mixture was investigated by a conductivity test. The microstructure of anti-freeze material with or without carrier was observed with SEM. In addition, the road performance of asphalt mixture with or without carrier was compared. Results show that the slow-release property of anti-freeze material increases and the salt-releasing speed in asphalt mixture reduces with the addition of carrier. However, asphalt mixture without carrier has low water stability and does not meet the code requirements. The research shows that the carrier in anti-freeze material can enhance the durability of the anti-freeze pavement and can be used as a good filler to improve the road performance of the anti-freeze asphalt concrete.

Composite member refers to the cross section formed by two different concrete combinations or the combined cross section formed by concrete and steel combinations. In this paper, a numerical model for the analysis of stress redistribution caused by creep is established on the basis of the deformation coordination relation of composite section, which is the cause of additional stress induced by creep. The validity of the numerical simulation method is verified by quoting the additional theoretical value of internal force caused by creep of section stress redistribution based on the concrete aging creep theory in the existing literature, and the variation rule of additional internal force of section stress redistribution under different strength and age is given according to the numerical simulation method.

The connection section of the freeway exit ramp and the adjacent road on the ground is an important node in the conversion of freeway and ground road traffic. According to a large number of statistics at home and abroad, the accident rate of the exit ramp is about twice that of the entrance ramp. Studying its safe geometric design method is of great significance to improve the traffic circulation efficiency and traffic safety at the expressway exit. In this paper, the factors affecting the safety of the exit ramp are summarized and analyzed. The factors affecting the traffic safety of the exit ramp are evaluated and analyzed, and various safety improvement suggestions are proposed. Studies have shown that the safe geometric design of joints is a simple and practical approach to traffic design. It has certain reference significance to the traffic safety design of various intersections in China.

Super-hydrophobic and self-cleaning coating has the advantages of economy, beauty, and environmental protection. It has been widely used in many fields, such as high-rise buildings, curtain walls, bridges, cars, and wind power generation. However, it is hardly used in the tunnel engineering. This paper demonstrates the feasibility of super-hydrophobic materials in the application of tunnel wall descaling in that it promotes the drop rolling off, influences the formation process of dirt and reduces the adhesion of dirt. The main factors influencing the adhesion of “dirt-wall” were analyzed with respect to roughness of materials and chemical composition changes. The SEM images and contact angle measurement of the three hydrophobic coatings selected in this project were analyzed. The super-hydrophobic materials were applied on the pre-laying of Wanjunhui Tunnel test road. It is proven to be effective in terms of bonding strength, water resistance, and fire resistance. It’s concluded that it’s of great value to apply super-hydrophobic and self-cleaning technology for inner wall of tunnel.

The dynamic modulus of HMA is a primary input for pavement design. However, usually, the measured dynamic modulus is not available in the design phase. Default or predicted dynamic modulus is commonly used for most newly built highways. In this study, comparisons have been made among dynamic modulus predictive models, i.e., Witczak model, modified Witczak model, and Hirsch model, focusing on the cold region HMA.

Crumb rubber modified asphalt (CRMA) has been widely used because of its physical and environmental benefits. Many researchers have studied the aging of CRMA, but most of them have focused on the overall research of CRMA. Little research has been conducted on the effect of base asphalt aging on performance of CRMA. In this study, the aging effect of base asphalt on the performance of CRMA has been studied using the thin film oven test (TFOT). The results show that thermo-oxidative aging of base asphalt has a negative effect on the properties of CRMA. The aging of base asphalt resulted in the reduction of penetration, ductility, and elastic recovery of CRMA prepared as well as in an increased softening point and viscosity, and further deterioration of plasticity of CRMA. The latter effect continued to be more serious as the degree of aging of base asphalt increased.

The increase in urban road traffic noise pollution has seriously affected people’s living, physical health, and socio-economic development. This study selected typical areas as measurement points for multiple noise monitoring to evaluate noise nuisance and psychological fluctuations. The propagation law of urban traffic noise was investigated by monitoring the sound level in the transverse, longitudinal attenuation process at different pavement materials. In order to evaluate the noise pollution along the urban pavement accurately, a large-scale road traffic noise mapping system based on GIS and Cadna/A was developed to display traffic noise pollution. The road traffic noise mapping vividly showed the noise pollution around the monitored area using less data, which is of great significance for the evaluation of urban road traffic noise.

In this paper, mainly aiming at plateau cold areas, introduces the research on temperature-regulating asphalt pavement based on phase change materials (PCMs), elastic self-stress ice-breaking asphalt pavement, and environment-friendly self-melting deicing asphalt pavement. The driving conditions of the road surface in cold plateau areas are ameliorated to promote road driving comfort, durability, and safety through the application of some technologies.

Improving the thermal effect of urban pavement is one of the methods to solve the urban heat island effect and the problem of urban near-surface human comfort. In this paper, the temperature field of the pavement under different thermal properties is simulated using the fluid-solid coupling model. According to the radiant heat effect of the road surface and the calculation method of convective heat transfer effect, the all-day heat effect of the asphalt pavement under different thermal properties parameters is calculated. The influence of thermal properties on the near-surface exothermic effect of urban pavements is also analyzed. The optimal combination of pavement material parameters is obtained by the orthogonal test method to reduce the urban heat island effect.

A multifunctional material can be used to protect and monitor the degraded concrete infrastructure, providing a self-sensing capacity that is designed to specifically diagnose cracking. Strain-hardening cementitious composites (SHCC) presents superb tensile ductility and pseudo strain-hardening property. In this study, the electrical and mechanical properties of SHCC incorporating carbon black (CB) nanoparticles and air entraining agent (AEA) are examined. The electrical response of SHCC, HFA-SHCC (high fly ash SHCC), and CB-SHCC under direct tension is investigated using the four-point probe test. The results show that the bulk resistivity of all specimens increased with the crack propagation, and the increase ratio in the inelastic phase is much higher than that in the elastic phase. Furthermore, CB-SHCC specimens showed the most sensitive response to first cracking, which can be potentially utilized to detect micro cracking by means of variation in resistivity.

Interchanges can switch between different highways, but there are many limitations on linear design that led to accidents. In order to further improve driving safety and reduce the occurrence of traffic accidents. This paper takes 11 interchanges in Shanxi Province as the research object. According to the design principle of different combinations of horizontal and vertical alignment, the analysis area is divided into eight basic sections. A linear analysis of the trunk line, ramps, and distribution lanes was performed to modify the basic model of crashes prediction (SPF) and establish the crashes correction model (CMF), and then applying the T test and the F test for significant test. This paper takes the Qinglong interchange of Sichuan Province as an example to verify the effectiveness of the crash’s prediction model. The results show that the crashes prediction model constructed in this paper is significantly improved compared with the original model.

Standardized traffic signs can significantly improve the efficiency and safety of road use. However, in the process of traffic engineering design and construction, the standardization drawing and simulation of traffic signs have not been modularized yet, resulting in inconsistency in format and content. In view of the above problems, this paper develops the development of standardized traffic sign drawing module based on AutoCAD. It is a secondary development of AutoCAD in the visual LISP development environment combined with ActiveX technology. The research results show that the developed standardized traffic sign drawing module can meet the needs of users to the utmost extent. At the same time, the format, and content of the traffic signs are unified, thereby further improving the road traffic system and establishing a more reasonable and standard traffic sign system.

This study is to evaluate the effects of asphalt binder and anti-rut agent on the low-temperature crack resistance of HMA. Influencing factors considered in this study include: one type of HMA, two types of asphalt binders, and an anti-rut agent at three dosages. HMA bending test of small beam at low-temperature and thermal stress restrained specimen test (TSRST) were used to evaluate material performance. Results indicate that the temperature of low end of PG has a significant effect on low temperature performance of HMA. Adding anti-rut agent can negatively affect the low-temperature crack resistance of HMA.

The traditional lane-changing trajectory planning model divides road conditions into obstacle-changing trajectory design and barrier-free lane-changing trajectory design. Due to the constraints of dynamic obstacles, the obstacle-lane-changing trajectory design will introduce a larger amount of calculation than the barrier-free lane-changing trajectory design. This increases the delay and reduces safety. In order to overcome these shortcomings, this paper proposes a cooperative scheduling lane changing model for intelligent connected vehicle based on deep reinforcement learning. The collaborative scheduling algorithm acts on the trajectory planning layer to unify all lane-changing trajectory designs into barrier-free trajectory designs to reduce the amount of computation. The lane change decision maker based on deep reinforcement learning determines the optimal moment of the cooperative scheduling algorithm. Finally, the paper verifies that the proposed model has a significant improvement in traffic efficiency by comparing with the traditional lane changing model under different traffic density.

To explore the mechanism behind carbon dioxide emission of automobiles on the streets of Chinese counties, the paper studied some basic factors such as random factors, desired speeds of drivers, and vehicle lengths of automobiles, which might influence carbon dioxide emissions, by applying MOVES (motor vehicle emission simulator), based upon calculation of distributions of average travel speeds of automobiles, obtained from microscopic traffic simulations. It was found out that although random factors impacted movements and speeds of individual vehicles, their influences upon carbon dioxide emissions of automobiles were very insignificant and could be ignored. Desired speed distributions had some obvious influence upon carbon dioxide emissions of automobiles. With increase of desired speeds of automobiles, carbon dioxide emission factors would decrease. Finally, vehicle length distributions of three Chinese counties were calculated and although the distributions were different, their influences upon carbon dioxide emissions were so small that they could be ignored.

In this paper, an accurate and effective method is proposed to construct the driving cycle of electric vehicles in urban areas, using Xi’an as the research object. Four main parts comprise this work: developing test routes, data collection, data processing, and driving cycle construction. According to the road topology and traffic flow survey results of Xi’an, the test route was developed. Then, driving data was collected. Solving the problem of complex process and low clustering accuracy in traditional fragment partitioning, dimension reduction, and clustering data processing methods, the velocity-acceleration grid was proposed to directly divide the data. With the single classification condition and the loss of small probability events in the traditional Markov method, the Monte Carlo simulation optimization method is proposed. Finally, the construction the passenger car driving cycles in Xi’an was completed. Compared with the driving cycles built by other methods, the proposed method is relatively accurate.

A questionnaire survey was conducted among 110 sample members who met the gender and age distribution in China. Individual factors influencing decision-making behavior were obtained through significant analysis to study the emergency decision-making psychology and escape behavior characteristics of passengers after bus accidents. The factors mentioned above are studied and trained through the theory of naive Bayesian to simulate the subconscious escape decision-making types of personnel. According to the environmental factors, the judgement matrix is constructed according to the analytic hierarchy process (AHP), and the decision-making model of passenger escape after bus accident is established. Designing the bus accident escape experiment, the experimental results are in good agreement with the model prediction results. Research shows that the emergency decision-making psychology of passengers after bus accidents is largely influenced by individual characteristics and acquired experience.

Cooperative vehicle platooning composed of several vehicles can make full use of road resources, improve traffic safety, increase road traffic capacity, and reduce exhaust emissions. However, the longitudinal control of the platoon is always affected by the delay of vehicle actuators and inter-vehicle communication. In this paper, the platoon model is established considering the vehicle actuators’ lag and inter-vehicle communication delay, integrating the information of the preceding vehicle and the leader vehicle of the platoon, and the platoon longitudinal control method considering delays is designed. The controller parameter boundary conditions which can keep the queue stable are derived based on the Lyapunov stability theory. A platoon composed of ten vehicles is used for simulation analysis and results show that the proposed longitudinal control method can make the controlled vehicle respond quickly to the change of the motion state of its front vehicle and the leader vehicle, and ensure that the vehicle spacing is stable at a safe level with a maximum error of 8%. In addition, when the first vehicle encounters emergency conditions for braking (deceleration reaches 0.8 g), it can also ensure the safety of the platoon and avoid rear-end collisions.

The construction of parking reservation system based on traveler’s demand has become an important part of parking demand management. Based on reservation mechanism, the parking reservation system was prompted and the motivation of different parking reservation strategies (such as time sharing and real-time) was revealed. The intervention form in parking reservation behavioral model was discussed. Stated preference survey was carried out to obtain the information demands of parking reservation, preference, and parking reservation intention. Parking reservation decision model was established based on binary logit (BL) model and the boundary conditions of traveler’s shifting from conventional parking service to reservation service was analyzed. The results show that factors such as parking fee, reservation fee, and remaining ratio of parking space have significant influences on parking reservation behavior at 95% confidence level. The research an provide a theoretical basis and data support for the construction of parking reservation systems.

Based on system dynamics simulation, the stability of dual-channel supply chain under retailer centralized management inventory model is studied. Firstly, the inventory coordination of centralized dual-channel supply chain is studied to determine the optimal inventory under uncertain demand and safe inventory conditions. Secondly, the logistics and information flow paths in the feedback loop of the system are deduced, the causal feedback relationship of the system is analyzed, and the SD model of the dual-channel supply chain is constructed. Finally, the Anylogic simulation software is used to simulate the inventory, profit, and shortage level of the whole supply chain system, retailers, and manufacturers. The results show that the stability of the dual-channel supply chain is relatively poor when demand changes; when channel preference ratio changes, the stability of the retail channel changes, while the electronic channel is not affected. The study can provide a reference for the optimal decision-making of dual-channel supply chain managers.

Contactless power supplies transmit power with no mechanical contact, which is flexible, safe, and reliable. The contactless characteristics are very suitable for high speed maglev train driven by long stator linear synchronous motor (LSM). Here, we analyze the problems of vehicle power supply for existing electromagnetic suspension (EMS) high speed maglev systems operating at both low and high speed. At low train speed, a separate transformer is used to transmit power. We propose several types of separate transformer structure and compensation methods of primary and secondary coils. Generators installed on the train are used to generate power by the cogging effect of the long stator LSM when the train is running at high speed. We also discuss the working mode of the contactless power supply system at all speeds of the maglev train.

China is in the period of promoting the modernization of logistics transportation equipment, and developing standardized and van-type freight vehicles are the trend. With the release of national standards “Limits of dimensions, axle load and masses for motor vehicles, trailers, and combination vehicles” (GB 1589-2016), “Technical specifications for safety of power-driven vehicles operating on roads” (GB 7258-2017) and “General technical requirements for centre-axle trailer” (GB/T 37245-2018), the further advancement of the national non-standard vehicle transport vehicle management and the development of modular vehicle combination trials, the centre-axle trailer combination is gradually becoming one of the main models of road freight transportation in China, which marks the addition of a compliant, reasonable and standardized automobile train model to China’s medium and long-distance cargo transportation. This paper analyzes the advantages and disadvantages, design, and use points of the centre-axle trailer combination, and provides references for the standard development technology of this combination.

It is a new way to deal with unbalanced distribution of passenger flow to adopt the stop-skip scheme. Firstly, this study regards saving passengers’ travel costs and operating expenses as targets, and proposes a bi-objective nonlinear mixed integer programming model with one 0-1 decision variable, which evaluates the costs under different stop-skip schemes. Using the genetic algorithm with the ideal point method, find the optimal scheme under given distribution of passenger flow. Secondly, this study takes the direction of Fenghuangxincun—Wanshengwei on Guangzhou Metro Line 8 as a case for application and verification. Compared with the all-stop plan, it is found that the travel time costs of passengers and the operating expenses were saved by 15.9% and 16.3%. These results show that the model has advantages in terms of saving passengers’ travel costs and operating expenses and speeding up turn-back cycle.

In the regulation layer, the automobile cruise control system (ACCS) is responsible for transient lateral maneuvers and closely related to executing steady state. This paper develops an improved differential evolution algorithm (IDEA) to deal with the control parameter optimization problem for the ACCS. Based on the classical Bayesian decision strategy, a prior probability based sequential chromosome generator is built to partition the problem space and approach the probable solution domain as close as possible. By mimicking the crossover and mutation behaviors among chromosomes, an online adaptive search method is proposed. The IDEA can effectively deepen the search area and simplify the parameter tuning process to get a well-performed ACCS. A nonlinear automobile model is used as a test bed to verify the feasibility and efficiency of the proposed method. Numerical simulations show that the IDEA optimized ACCS has good performance in terms of both steady state maneuvers and transient maneuvers.

In this paper, the two-degree-of-freedom kinematics model of vehicle is established, and the differential equation of a vehicles two-degree-of-freedom model is discretized and linearized. A trajectory tracking controller based on model prediction algorithm is designed, and a reasonable constraint function is established based on the experiment, attempting to balance the accuracy of path tracking and vehicle stability. The algorithm is verified by Carsim and Simulink platform. The results show that the trajectory tracking controller based on model predictive control can track the preset trajectory better when it is below 5 m/s.

As an environmentally friendly means of transportation, Shared bikes are favored by students in colleges and universities for their convenience, speed, health, environmental protection, and low cost. With the development of bike-sharing industry, it brings about endless problems. Based on the city of Zhengzhou in Henan province university students as research object, through questionnaire investigation, interviews, comparative analysis and SWOT analysis on campus sharing of major brand bike usage survey analysis, and the present problems of the campus sharing a bike from the government, enterprises and users, and other point of view put forward with coping strategies and establish a model for the problem of trouble bicycle recycling scheduling.

For the purpose of accurately capturing the traffic state on expressways, this paper develops a multistep prediction algorithm of flow speed on consecutive sections of expressways based on the state space model. First, the Van Aerde fundamental flow diagram is selected to replace the trapezoid structure utilized in the original cell transmission model (CTM), and its applicability under unequal cell lengths is discussed. Second, based on the modified cell transmission model (VA-CTM), the state space model is constructed by taking density as the state variable, and space mean speed and flow as output variables. In this process, we modify the state equation in consideration of the correlation between state and output noise vectors. Subsequently, a multistep prediction algorithm is designed based on the extended Kalman filter. Finally, several consecutive sections on the Western 3rd Ring-Road Expressway of Beijing are selected as the case study site to validate the proposed model.

Small package express vehicle dispatching is a new problem within the transportation sector, which has positive significance for improving the allocation of vehicle resources in passenger transport enterprises. This paper considers the optimal matching of goods and vehicles from a global perspective, and constructs a mathematical model aiming to maximize the sum of matching degree between small package express transportation and vehicles, according to the demand of small package express service, the remaining capacity of the luggage compartment and time window constraints. Then, a two-stage algorithm is designed. In the first stage, an algorithm based on the matching degree of small express vehicles and goods is proposed to calculate the matching degree between goods and each bus. Next, an algorithm of small-part express allocation is proposed to solve the problem of cargo car selection. The validity of the model and design algorithm is validated by experiments with randomly generated data.

With the growth of volume and the increasing articulated trucks on expressways recent years, multi-lane expressways are increasing and the separation of truck and passenger car (“SPCT”) has become a trend of multi-lane expressways and interchanges. The exclusive truck ramps (hereinafter called “ETR”) in the “SPCT”-interchange were taken as object, and their longitudinal slope was studied combined with the dynamic performance of large trucks. A 6-axis articulated truck fully loaded was taken as the target vehicle. Based on dynamic parameters, a climbing model of the truck was established. According to relevant parameters, the maximum longitudinal slope and slope length of “ETR” in the snowy areas were analyzed and the recommended values of the maximum longitudinal slope for the “ETR” were proposed. Finally, “Trucksim,” a dynamics simulation software, was used to analyze the characteristic parameters of the 6-axis articulated truck running on “ETR.” Verification results show that the longitudinal slope proposed of the “SPCT”-interchange is reasonable, and it’s suitable in snow areas.

The truck escape ramp is a type of traffic safety facility for out-of-control trucks. It is a clump of pebbles that is located alongside the main road. As the pebbles are randomly shaped in arrester beds, few studies were conducted on the property of the pebbles. In this paper, a two-dimensional discrete element pebble shape construction method was built, and parameters of the built pebble DEM model were calibrated using compression tests. Next, simulations of different pebble shapes were conducted, and signals of the pressing plate displacement and contacting forces were recorded. The results indicate that the built two-dimensional DEM model well monitors the real pebble shapes, and repetition simulations verified the built pebble DEM model micro-scope parameters. The built pebble DEM model could be further used in the simulation of truck tires rolling into the arrester beds and the design of truck escape ramps.

To improve the in-plane impact resistance of trichiral structures, a re-entrant trichiral structure (RTH) was constructed by adding re-entrant structure into trichiral honeycomb (TH). The deformation modes, energy-absorbing capacity, and auxetic performances of RTH under different impact velocities were studied by using the commercial software LS-dyna. The results show that re-entrant structure can enhance the localized “necking” deformation of TH under medium- and low-velocity impact, and a “\” -shape deformation mode appears during the entire collapse process. Furthermore, it is found that the stress-strain curves can be divided into two stages: the plateau region and stress increasing region. The plateau strain ε_p and densification strain ε_d are gradually increased with crushing velocity. Compared with the TH, the specific energy absorption of RTH is 61.16% higher under low-velocity impact and 58.9% higher under medium-velocity impact. This study provides a novel path for the design of hybrid deformation mechanism auxetic honeycombs.

In the high-speed maglev transportation system, the protection of train operations is provided by the cooperation of the on-board operation system and the ground operation system. The interface between on-board operation system and region operation system is a logical interface, which is implemented by way of a wireless communication system and safety communication module. In this paper, based on the safety concepts, regulations, and the design of train safety protection, the faults, errors and failures of the interface between on-board operation system and region operation system are discussed, including the redundancy loss of critical components, the physical channel failure and the logical channel loss caused by data validity or safety check reject, etc. Based on the interface structure, analysis of single point faults and the scheduling of failure, a state transition diagram is proposed.

With the surge of traffic volume, more four-lane freeways have been converted into eight-lane freeways, which leads to a rise in vehicle driving freedom and complexity. Correspondingly, lane management strategy is employed to regulate traffic operation and thus modify road capacity. This paper investigates basic sections of an eight-lane freeway as the research object. Based on the operation data of the Shanghai-Nanjing freeway, a vehicle classification method taking rear-end spacing as the index was promoted, classifying vehicles into four kinds. Then by adopting empirical analysis and cellular automaton simulation model improved on vehicle division and lane changing rules, the eight-lane freeway capacity was studied and verified. Moreover, the impact of truck ratios and lane management strategies on capacity was specified. By conducting this research, it will be beneficial to control eight-lane freeway flow reasonably as well as improve operational efficiency and safety.

The rapid increase in the number of motor vehicles has intensified environmental pollution, and if the actual traffic information is used to carry out the route planning of the lowest emission for motor vehicles and achieve environmental protection, which can effectively improve the above problems. Based on a large number of actual vehicle experimental data, this paper establishes a vehicle emission measurement model. Combining the A* algorithm with the vehicle emission measurement model, this paper completes the path planning simulation analysis based on the lowest emission target. The results show that due to the actual traffic environment of the planned route and the congestion at the intersection, there is a big difference in the exhaust emissions of vehicles with different travel paths between the same starting and ending points. The vehicle emission minimum route planning model constructed in the paper can effectively reduce vehicle exhaust emissions during the trip, and provides new ideas for improving environmental pollution.

The rapid development of free-floating bike sharing (FFBS) has greatly facilitated residents’ short-distance travel, which has significantly alleviated urban traffic pressure and promoted low-carbon travel. In view of the problems such as difficulties in bike rental or damaged bicycles in actual operation, we studied the multi depotstatic bike rebalancing problem with time windows considering the recovery of defective bikes (MDSBRPTWRDB), a mathematical model where the objectives are to minimize the redistribution costs, and maximize the customer satisfaction was established. We designed a hybrid algorithm combined with the simulated annealing algorithm and genetic algorithm using a simulation instance of free-floating bike sharing system. We demonstrate that the mathematical model and heuristic algorithm is practical and effective.

As the main and even the only mean of transportation in rural areas, rural roads are the basic and leading conditions for rural vitalization. Research to determine a reasonable road network scale has great significance while supporting the government to make cost-effective decisions. Based on the analysis of the rural road development situation under the rural vitalization strategy, this paper simplified the “4-B rural roads” costs into existing and new ones and then studied the decision-making mechanism of rural roads scale from the perspective of government.

This paper studies the early warning of traffic jams. According to the cause of traffic jams on the highway, the traffic jam early warning index system is established. Based on the method of fuzzy evaluation, the possibility of traffic jams is evaluated. Then issue the corresponding early warning signals in the light of the obtained warning level so as to achieve the purpose of predicting traffic jam. Afterwards, Huangyan expressway was selected to verify the feasibility of the model presented above for empirical analysis. This paper proposes a method about early warning of traffic jams on the highway, which contributes to reducing the probability of traffic jams on the highway.

The study of driver’s driving intention is of great significance to improve vehicle safety warning technology, auxiliary driving technology and optimization of vehicle control strategy. By analyzing the driving environment data collected by the Internet of vehicles monitoring platform, the driving environment perception model based on the long-term memory network (LSTM) is established to judge the driving environment. After that, the symbolized environmental result combined with vehicle state parameters collected by the sensors are used as input to the driving intention identification model based on LSTM. Then the dynamic driving behavior and driving intention of the driver are analyzed. The results show that, based on the dynamic model LSTM, the driving intention identification model considering the driving environment produce a higher accuracy.

It is agreed that freeway has higher capacity than urban streets because of its higher speed limits and relatively closed traffic condition. However, the existence of low-speed vehicles on freeways, as well as resultant frequent lane-change and overtaking behaviors of other vehicles, will greatly decrease the capacity. And low-speed fines have been imposed on some freeways in China. In order to further clarify the impact of the characteristics of low-speed vehicles (i.e., speed, proportion, and driving lanes) on traffic capacity and overall traffic state, in this paper we conduct extensive simulation experiments using VISSIM 11 software. Different demand levels and freeways with different number of lanes are constructed by VISSIM to obtain comprehensive results under diverse proportion of low-speed vehicles. This study can help analyzing the impact of vehicle speed composition on traffic state, as well as providing guidance for the formulation and application of relevant traffic policies.

People usually circle around for quite a long time in a large parking garage to find an open parking space due to unevenly distributed available parking spaces. In this paper, we propose a system which uses the convolutional neural network deep learning approach to analyze videos recorded at different places in a large parking garage. Our system is able to monitor the availability of each parking space and provide useful information to users about the open parking spaces in real-time. To evaluate our system, we conduct an empirical analysis based on the data we collected from a parking lot at IKEA in Wuhan. The evaluation results show that our parking space management and allocation system can reduce ineffective vehicle detours, shorten the average time spent on finding an open space. This work also offers useful and deep insights to solve parking problems at centralized and large-scale parking garages.

As an effective means of vehicle collision avoidance, the automatic emergency braking (AEB) system has become the research focus for heavy commercial vehicles in China recently. However, the existing AEB system control algorithms do not consider the influence of road conditions and cannot meet the need of emergency braking for the dangerous goods transportation vehicle. Therefore, this study proposes a vehicle automatic emergency braking control strategy based on real-time perception of road pavement attachment. Firstly, the real-time estimation method of road adhesion coefficient based on vehicle 3D lidar is put forward. Then, an automatic emergency braking strategy considering road pavement attachment is established. Finally, a test under different dangerous conditions is carried out. The results show that the dangerous goods transportation vehicle which installed the proposed strategy can provide the best braking timing and braking strength. This research will greatly improve the safety of highway transportation.

Traffic volume detection is the base for traffic management and even smart traffic construction. This paper proposes a method based on convolutional neural networks (CNN). Considering the camera always being fixed during traffic volume detection, a shallow residual neural network (ResNet) model is proposed in this paper, which uses road video data to train model parameters and extract vehicles feature. After training, this paper uses the model to identify the vehicles and a core correlation filter is proposed to track the target. Finally, the traffic volume count method is determined by judging whether the target passes through the region of interest (ROI). Compared with other traffic volume detection methods, this method is more suitable for classifying and counting vehicles in free flow because of its reliability and light weight. The experiment shows that the model has the recognition accuracy of 95.83% and the effective count rate is 88.37%.

Wheel alignment parameters directly affect vehicle handling stability. Wheel alignment parameters include wheel toe-in, camber, caster, and kingpin inclination (SAI). Wheel toe-in and camber are between wheel plane and car longitudinal plane. The angle can be directly measured by the sensor, and the kingpin is the virtual axis used to restrain the steering of the wheel in the general position of the space. The traditional method cannot directly measure, and the measurement is usually performed by using an approximate linear model. Measurement time is long and the resulting measurement error is large. In order to realize the rapid and accurate detection of wheel positioning parameters, the binocular stereo vision theory is used to establish a mathematical model for measuring wheel positioning parameters, and to realize automatic non-contact measurement of wheel positioning parameter detection.

A four-degree-of-freedom system dynamics equation of heavy trucks is established based on vehicle theory and force analysis. In this equation, steering wheel angle and vehicle longitudinal velocity are input variables, and vehicle lateral velocity, yaw rate, roll angle, and roll angle rate are output variables. The dynamic model is solved by using state space theory; time domain simulation analysis is carried out by using Matlab software. Rollover stability for heavy truck is analyzed and evaluated under the simulation of different steering wheel angle input by the analysis of lateral acceleration and root locus of yaw rate and roll angle. The results show that rollover stability for heavy truck can be evaluated effectively by numerical analysis of lateral acceleration and root locus analysis. The simulation results give a method of roll analysis for heavy truck in turning conditions, and also can provide some technical support and useful reference to driving safety.

The categorization of high-speed railway passenger value reflects the demand differentiation from passengers. This is essential for optimizing high-speed railway price strategy and the revenue. This paper extracts RFM of passenger value as the core features, analyzes the weight for each core feature based on AHP and high-speed railway expert strategy, and adopts fuzzy clustering algorithm for clustering analysis, finally comes out the passenger value segmentation model. Based on the passenger flow for Beijing-Shanghai high-speed railway, this paper divides the passengers into five categories including high-value passengers, growth passengers, commuters, potential passengers, and general passengers. This passenger value segmentation and portraits can be applied in revenue optimization and provide better experience for passengers.

The economy of Pakistan has fallen considerably in the last decades GDP by 6.2%–3.3%. The condition of Pakistan railways (PR) has dilapidated since the start of the 21st century. The CPEC project emphasizes on revitalizing the rundown situation of PR by up-gradation of the entire main line (ML) railways. The project is divided into three phases, i.e. ML1, ML2, and ML3. In this study, we have tried to give an overview regarding the potential impact of ML1 on the PR. The goal of this study is to highlight the root causes that have affected the PR, signify the causes of railway accidents in Pakistan, and provide recommendations. This is done by considering the importance of ML1 on reducing the rail accidents and resurgence of Pakistan’s economy. The salient features of ML1 are mentioned alongside the potential opportunities this new project implements in the PR system.

Traffic safety has become a severe problem on freeways in China, thus, it is important to establish a real-time crash risk model to identify traffic conditions causing crashes. In this study, we explore the real-time crash risk for urban freeways in China and obtain dynamic crash risk level. Crash and their matching traffic sensor data from a Beijing section of Jingha expressway in eight 5-min intervals between 0 to 40 min prior to crash occurrence was extracted during eight different periods. The crash risk value under different data conditions was defined. Then, a real-time crash risk assessment model using decision tree method and adaptive neural network fuzzy inference system (ANFIS) was proposed. Comparing several real-time crash risk assessment methods, such as logistic regression, decision tree and supported vector machine (SVM), it was found the proposed method had higher precision than others. This study can be applied to monitor real-time traffic risk on urban freeways, to assist traffic control decisions and reduce traffic accidents.

Driver drowsiness is an important public safety issue. The difficulty in monitoring the driver’s individual differences makes the error rate of fatigue judgments of different individuals larger. Nine steering wheel variables and three reaction time variables were measured as participants drove a fixed road course in a high-fidelity motion-based driving simulator. Drowsiness levels of participants were judged by the expert scoring method combined with the driver’s self-described method using the Stanford sleepiness scale. For each driver, a hidden Markov model (HMM) was trained and recognition of a given observation sequence was performed to score each drowsiness driving model. Ellipse, NMRHOLD, PNS, SW_Range_2, RT₁, and RT₂ were selected through one-way repeated ANOVA as the characteristic of drowsiness. Sixteen HMMs were identified by data sequences of 16 drivers and the accuracy rate was 87.5%. It is recommended that HMM could be used to detect driver drowsiness and address the inaccuracies caused by individual differences.

Bogie frame is one of the main load-bearing components of rolling stock, its structural reliability and wheel-rail force interact with each other. By establishing a rolling stock rigid-flexible coupling model, the stress level of the bogie frame in different working conditions is analyzed, the bogie frame stress level changes before and after the structural improvement and the impact on the vehicle operation safety are studied. The results show that after structural reinforcement, the stress level at the connection between the base of the positioning arm and the lower cover plate decreases significantly. With the increase in train running speed, the stress improvement effect decreases gradually. In addition, the derailment coefficient and wheel load reduction rate under the improved frame have no significant change, but the value decreased, which indicates that the improvement of frame structure can improve the train operation safety.

Unreliable delivery times and the low profitability of railway freight enterprises have become the key factors restricting freight railway development. Based on the existing technology for the preparation of freight train marshalling plans, this paper analyzes the organization and operation of direct trains departing from the loading site and proposes operating conditions. For the remaining traffic flow departing from the loading site on a daily basis, after the direct trains, this paper shows an income method to improve efficiency and increase the revenue of railways. For the marshalling plan, the hourly cost of the assembly car, the unit cost of freight car adaptation, and the specific cost for the consumption time in the process of the flow of the car is estimated and the optimization model group of the railway collection and distribution service network is established. The accuracy of the model is verified with a small-scale example using CPLEX.

Dynamic loads of vehicles on bridges is a main reason for bridge damage. The vehicle-bridge coupling vibration is important to the safety of bridge structures. Based on the Lagrange equation, the dynamic model of a 10-axle vehicle with 5 degrees of freedom is established, along with a simply supported beam bridge model. The numerical simulation of bridge deck roughness is carried out, and a dynamic model of vehicle-bridge coupling system built by the equilibrium conditions between the vehicle and the bridge displacement is built. The influence of vehicle speed, tire stiffness, and bridge deck irregularity on the dynamic response of the bridge is analyzed by numerical examples. Results show that in the range of a certain speed, the maximum span of the bridge is changed with the increase of the speed, and it will increase with the increase of velocity. When the tire stiffness changes, the maximum displacement of the bridge increases with the increase of tire stiffness.

Variable message sign (VMS) seems to be a good way for traffic information releasing in fog weather because of active luminous. However, it is still not quite clear whether the normal text height displayed on VMS can meet drivers’ visual requirements under fog weather. A total of 33 participants were recruited to test the effects of text height on drivers’ visual characteristic by dynamic driving simulation experiments. Comparison results showed that the distance between driver reading finish point and VMS (DRFV) increased significantly as the text height increasing for both normal and fog conditions. Under the fog conditions, DRFV, and visual cognition time were slightly better. Vehicle speed at reading finish point and driver subjective evaluation score for VMS under these two weather conditions were not found a significant difference. Drivers tended to slow down in advance to compensate for reduced visibility in fog conditions.

To examine the aerodynamic characteristics (AC) of high-speed-train (HST) running on half-embankment and half-cutting transition section (HEHCTS) under crosswind, a CRH3 HST is taken as the object. The AC of the HST running at four speeds and under crosswind action with five speeds are numerically investigated, and the correlation changing rules between the HST AC and the HST speed or crosswind speed were obtained. When the HST runs on HEHCTS at different speeds and under different-speed crosswind, the lateral forces (LFs), and overturning moments (OMs) acted on the head carriage are the largest. With the increase of crosswind speed or HST speed, the increases of the LFs and OMs on the head carriage are the largest. When the HST speed is 350 km/h or the crosswind speed is 30 m/s, the AC parameters of carriages have cubic relationships with the crosswind speed or HST speed. Crosswind speed has greater influence on HST’s AC than HST speed.

Real-time, on-demand mobility systems have gradually changed transportation. However, they often cannot meet all demands at peak times, especially for passengers entering or leaving the airports. The popularity of ‘sharing’ may solve such problems. For airport access, our research proposed a heuristic algorithm to provide a dynamic large-scale vehicle sharing method. Three different pick-up and drop-off strategies were compared to illustrate their behaviors under different circumstances, based on a representative high-demand case of serving all inbound and outbound passengers at Shenzhen Bao’ an International Airport. With the increasing passengers, first, it is more appropriate to choose Strategy 3, then when the number of passengers approaches the capacity, Strategy 2 changes to be better. This system makes it possible to provide decision support on the implementation of sharing services, which is considered a desirable way to create a low carbon city.

Type-C weaving sections, typically located near freeway-freeway interchanges, have high weaving demand volume which leads to severe bottlenecks. Ramp metering (RM) has emerged as a management strategy, and ample research has addressed ramp metering control algorithms. However, the application of RM control algorithms for type-C weaving sections has received limited attention. A modified local RM control strategy, which is specifically designed to improve the traffic conditions within type-C weaving areas, is proposed. ALINEA is used to study the proposed RM strategy at three C-type weaving sections located in Los Angeles, CA. VISSIM simulation results show the modified ALINEA strategy provides a better level of service and can improve the traffic operations within the type-C weaving area. An occupancy value of 30% for the type-C weaving section is recommended.

As the freeway develops rapidly in China, the problem of traffic accidents on the freeway, has become increasingly prominent. At present, the freeway traffic safety in China isn’t optimistic due to regional differences and other factors, which fosters the probability of freeway traffic accidents. This paper proposes a method of social network theory. This study applies the method to the freeway network and safety accidents in Jilin Province. The Ucinet software is used to analyze influencing factors of traffic accidents with centrality and cluster analysis, based on the internal relations and the status of each factor in an accident. This study can provide theoretical support for the evaluation index system and follow-up research on regional freeway safety protection.

The taxi is one of the main carriers of urban transportation. It is of great significance to improve its dynamic and economic performance by matching the parameters of vehicle powertrain system and optimizing energy management. Test data of two taxis for 1 week was collected in this study. Firstly, a Fourier filter was used to preprocess the test data. Through processing of taxi operation data, the difference of passenger carrying time and mileage between the weekdays and weekends, along with the travel laws of the peak and off-peak periods in Xi’an were obtained. Then we divided the test data into short strokes, extracted the characteristic parameters related to velocity, acceleration, and time of each short strokes, and reduced the dimension by principal component for the characteristic parameters. We then used cluster analysis. Finally, we constructed the driving cycle of Xi’an taxi by Markov-Monte Carlo simulation.

Currently, problems exist in port gathering and transportation modes, such as considering efficiency, neglecting environmental protection, focusing on scale expansion, and neglecting the transportation system coordination. Barriers between modes of transport and lack of communication channels lead to facility layout optimization difficulties. One factor for the construction of a hub port is the support of a collection and distribution network. The advantages of rail-sea intermodal transport are large capacity, high safety, low cost, and low emissions. The development of rail-sea intermodal transport promotes transport structure and facilitates regional export-oriented economic advancements. However, the proportion of port cargo carried by railway in China is low, and the capacity of the railway is insufficient, which restricts port development. By analyzing the freight demand of ports, this paper summarizes transportation mode characteristics. Using the Shanghai system as an example, this paper clarifies the necessity and presents the challenges of sea-rail intermodal transport construction.

The development of electric vehicles is occurring rapidly and demand for charging capacity is also increasing, so it is of great practical significance to study the charging behavior of electric vehicles (EVs). Based on the data of private electric cars in Beijing, this paper has extracted trip chains of EVs, considering a variety of potential influencing factors, and used different numbers to represent different charging behavior, then established two models: a kind of binomial logistic model (0-1/0-2 model) for single charging behavior and a kind of multiple logistic model (0-1-2 model) for non-single charging behavior. Comparing the two models, the results showed that the former model is better. This research will help to enrich research on charging prediction considering multiple charging options, and also be good for EVs drivers to make better charging decisions.

This paper presents an improved Dijkstra algorithm considering turn delays at intersections and establishes a UE traffic assignment algorithm based on the improved shortest path algorithm. The improved shortest algorithm identifies the turn delay by recording the previous node of each node in the path iteration process. And the time complexity of this algorithm is optimized from O(n2) to O(n log n) through heap optimization. A MATLAB program is realized and is validated through a real urban road network in Xi’an. The result shows (1) the shortest path algorithm used in this paper can sensitively identify intersection delays; considering turn delay in the shortest algorithm is more realistic; and (2) considering the intersection delay in the traffic assignment will not affect convergence performance too much. Moreover, the traffic assignment results may change when turn delays are considered.

As the transportation infrastructure for residents in China is rapidly developing and the integrated transportation hubs are vigorously growing, it becomes worthwhile to evaluate these hubs for maximum utility use. This paper aims to evaluate the factors that affect the functional utility of hubs. The system for passenger transportation function is constructed from five aspects: transportation organization, area service function, transfer distribution, information guidance, and auxiliary service. A fuzzy integrated evaluation method is used, and the weight determination is based on the analytic hierarchy process. The weights of evaluation indexes and fuzzy evaluation matrix are determined, and the results are calculated. Taking a high-speed rail-oriented integrated passenger transportation hub, the functional evaluation of the hub is carried out based on a questionnaire survey during holiday peak hours. The functional evaluation score of the hub is 3.032 points, whose function realization degree is relatively low, and it is consistent with congestion during holiday peak hours. The case study verifies the effectiveness and feasibility of the evaluation system.

This paper applies the regression discontinuity (RD) method to evaluate the effects of removing the London western charging zone (WCZ) on traffic flow and vehicle emissions. A total of 525 road sections are included for a period of 3 years (2008, 2010, and 2013). We first test the continuity of the confounders, and the results show that all confounders are continuous at the cutoff. Then the effects of implementing and removing the western charging zone scheme are estimated respectively. The results suggest that the AADT is reduced by about 31.1% within the western zone after implementing the WCZ scheme. On the contrary, without expansive congestion fees after calling off the WCZ, the number of vehicles within the western zone is increased by around 26.4%. Regarding the effects of the WCZ scheme on the emissions of vehicles, the results indicate that the vehicle emission of NO_X and CO₂ is decreased by around 29% and 33% respectively within western charging zone. In contrast, the emission of NO_X and CO₂ is increased by 24% and 21% respectively after removing the WCZ. After controlling for the time trend effect, the results indicate that there are residual effects of the WCZ on AADT and the vehicle emission of CO₂ (AADT: 4,412 versus 3,011, Time trend: −1,100; CO₂: 134 μg/m³ versus 70 μg/m³, Time trend: −30 μg/m³). However, such a residual effect on the vehicle emission of NO_X is not significant.

The connection environment of urban rail transit directly affects its service level. However, it is time-consuming and labor-intensive to optimize the connection environment of each station. In this paper, the clustering analysis of the connection environment using SPSS divides the Xi’an Metro line 2 stations into four categories. Based on the SP survey, the combination design method is used to obtain the residents’ evaluation for the connection environment of Stadium Station. Using MNL model to analyze the influencing factors of the choice of traveler’s connection method and TransCAD to verify parameters. Finally, this paper obtains the utility function of walking, bicycle, and bus in each connection environment and optimizes the design of the connection environment for stadium station.

In the “One Belt, One Way”, initiative presented by Secretary-General, Xi Jinping, the Chinese government encourages companies to internationaly invest so they can encounter new market challenges. Investment diversification and risk management of overseas financing projects have gradually attracted the attention of various parts of engineering. This article uses rail between China and Brazil as an example to analyze the full life-cycle risk of international rail investment throughout life-cycle risk analysis and potential business management risk research, assessed at each stage of the life cycle. The quality of a project is often influenced by many factors (variables), that are also impacted by many elements. This increases investment risks. Monte Carlo simulation is an excellent method for solving these randomnesses and proposing solutions.

Taxi’s can satisfy the high-level travel demands in urban areas, and the rational layout of urban taxis will affect both taxi passengers’ travel experience and taxi drivers’ income. Too many taxis in some regions with few travel demands will cause a waste of resources, while too few in other regions with lots of travel demands will affect the quality of service. Using minimum waiting time as the objective, this paper establishes an optimization model considering the dynamic change of taxi travel demand with time, and genetic algorithm is used to solve the problem. The feasibility of the model is verified by a simulation example. The effects of taxi scale and travel time on the layout and average waiting time are discussed. The method of determining the reasonable scale of taxi is given. The research results can provide theoretical support for the determination of urban taxi scale and reasonable spatial layout.

European firms have been using a combination of trucks and trailers in the delivery/collection of food products for years. Most of the studies on the truck and trailer routing problems (TTRP) assume the vehicle traveling time is a definite value. However, traffic congestion, road maintenance and traffic restriction, vehicle damage or other accident factors will lead to the uncertainty of driving time in practice. This paper studies a version of stochastic TTRP, in which travel and service times are stochastic, and a time window constraint is associated with each customer. This problem is formulated as a chance constrained programming model with recourse in terms of different optimization criteria. To efficiently solve this model, a heuristic based on Tabu searching, which accounts for the stochastic nature of this problem, is then proposed. Finally, a testing instances is established to investigate the algorithmic performance, the computational results is then reported and discussed.

The primary purpose of this study was to improve the efficiency of the land transportation network in Guanzhong plain urban agglomeration, and promote the construction of “the Belt and Road Initiative” by constructing a land transportation complex network model and analyzing its relevant characteristics. Firstly, the paper abstracted the expressway, national highway, provincial highway, intercity railway, universal speed railway, and high-speed railway in Guanzhong plain urban agglomeration, and constructs the land transportation complex network model in Guanzhong plain urban agglomeration by using the dual method and Gephi software. Secondly, the basic topological statistical characteristics such as average path length, clustering coefficient, degree and degree distribution were calculated, and analyzed quantitatively. Finally, the reliability characteristic of network connectivity under deliberate attack was analyzed. It was found that land transportation complex network possessed small-world properties but not scale-free properties. At the same time, the network tended to collapse due to 56% nodes deliberate attacks so it possessed good connectivity reliability. For the planning and management of the land transportation network developed by “the Belt and Road Initiative” and the goal of improving the network transportation efficiency, it was necessary to protect the lines with high degree, expand the lines with a low degree, and improve the robustness and reliability of the land transportation network.

In order to determine the impact of non-motorized vehicle waiting areas on the safety of signalized intersections, a random effect negative binomial model was established for typical three non-motorized vehicle waiting areas. The results show that the negative binomial distribution model with random parameters can make up for the defect that the traditional negative binomial model cannot describe the heterogeneity of the non-motorized vehicle waiting area, and the fitting result is better; the non-motorized vehicle waiting area in the form of BOX can effectively reduce the conflict; the volume of right-turning vehicles and straight-ahead no-motorized vehicles, the proportion of the large cars, and the proportion of the electric bicycles all have significant positive correlation with the traffic conflicts. The results are of great importance for improving the safety level of intersections.

Recently, the contradiction between people’s increasing diversified traffic mobility demand and social resources, energy, and environment has become more prominent. MaaS provides a more sustainable alternative to the use of private cars. The national government, relevant industry ministries, and local cities value it highly. This paper draws on the experiences of international researches on MaaS maturity index and MaaS existing typical cases to assess a cities readiness for MaaS implementation in China. Coupling this with the status quo and characteristics of the development of urban transport in China, MaaS maturity index of Chinese cities had been established from transport operators open attitude, people’s willingness, ICT infrastructures, government supporting policies and regulations, and data sharing. Based on the National Development Strategy of Guangdong-Hong Kong-Macao Greater Bay Area, MaaS maturity evaluation has been carried out in Guangzhou. Suggestions have been propounded for Chinese cities which could have insufficient readiness for MaaS implementation.

With respect to extensive existence of subjective factors of multiple attribute decision making (MADM) in risk assessment of ships, this study applies intuitionistic linguistic variables to depict the information of decision making with multiple attributes. Fuzzy Euclidean weighted averaging (FEWA) operator is applied to calculate the integrated assessment values of ships in order to reflect the group opinions. With integrated assessment values and membership degrees as the basic measures of risk, the ship safety expectation is obtained. As for case studies, five ships undergoing insurance assessment are ranked based on their safety expectation values. It is found that intuitionistic linguistic variables are useful for efficiently evaluating risk levels of ships.

The problem of insufficient coordination between urban rail transit and conventional public transport has seriously affected the operational efficiency of public transport. This study uses the ArcGIS software to analyze the density of four subway lines and bus lines in Xi’an. Combined with the urban POI hotspot distribution, the area where the POI is densely distributed, and the track is not coordinated with the conventional bus is used as the improvement area. The study shows that under the condition that the POI distribution is dense, the rail transit lines 2 and 4 in the northern part of the city have poor coordination with conventional buses. A method for improving the coordination of rail transit and conventional public transport based on the law of urban POI development is proposed. Adjust the bus route network in the improved area through specific cases to improve the coordination degree of the bus network.

With the rapid development of the city, the phenomenon of “separation of employment and residence” has become more and more serious. However, the continuous increase in commuting time will have an important impact on the subjective well-being of urban residents. Under this framework, subjective well-being in the field of trip time is selected to be studied in this paper. The data of the American Time Use Survey database is used to analyze the relationship between emotional feelings of trip and commuting time, using the ordered logistics regression model. Based on the regression results, the differences in emotional feelings of the trip among different groups are analyzed.