CICTP 2021: Advanced Transportation, Enhanced Connection

The metro system has been a mainstream of public transportation as it can alleviate the pressure of urban traffic. Before designing and constructing a new metro station, it is crucial to understand its daily ridership. In this study, a combined random forest has been proposed for prediction. The combined random forest fuses random forest and geographical random forest, which can combine their advantages to effectively reduce variance and bias respectively. To demonstrate the performance, three prediction accuracy indicators and Moran’s I test were employed. These models were implemented and validated on real-world metro station ridership data in Shenzhen, China. The results demonstrate the superior performance of the combined random forest based on the accuracy indicators. The reason is considered that the combined random forest accounts for the spatial heterogeneity according to the Moran’s I test.

Determining the value of existing infrastructure assets is critical to highway asset management. Economies worldwide have established asset valuation guidelines for better asset management. Different valuation approaches yield different values for a given asset based on different assumptions and mathematical formulations. The condition based valuation method of pavement asset is studied here, comparing the asset value of a 1257 km road network using three different valuation methods. The technical specification of road section was considered during the valuation process, and comparisons of asset value were studied over time. Pavement asset value variation with change of pavement condition threshold was also discussed. It is found that the asset value is not always positively correlated with pavement condition changes and multi-condition thresholds are suggested for different technical level road sections when using the fixed threshold method for asset valuation. This research provides references on selection of proper method for various management purpose.

With the increasing number of motor vehicles in China, road congestion is becoming more and more serious. Quickly identifying bottlenecks of urban road congestion and carry out causation mechanism analysis have become effective ways of alleviating traffic congestion. With the continuous development of intelligent transportation systems, abundant traffic data sources provide us with a lot of traffic information. Therefore, this paper uses floating car data to identify road congestion bottleneck sections. Then, the workplace and residence distribution and residents’ OD travel characteristics of the area near the road are obtained through mobile phone signaling data. Finally, traffic conditions and road facilities are analyzed by using the field survey data. The analysis results can be combined to carry out causation mechanism analysis on congestion identification results. This study can identify frequent bottleneck locations effectively and determine causes of traffic congestion from a deep level.

To predict bus arrival time at each stop with complex real-time traffic factors changing on the road, this paper proposes a dynamic arrival time prediction model for buses, using a combination of real-time taxi and bus datasets, with a kernel algorithm of statistical methods or neural networks to model the properties of dynamic road circumstances. This is a “rolling” prediction method, which uses the prediction result of previous step as the input features of the next prediction, to predict all bus arrival times of future stops. The result shows that the proposed dynamic model is applicable for bus arrival time prediction, where datasets with taxi are more accurate than without it. The possible bus bunching can be captured by checking the predicted time headway between buses from the same bus line, and long-time delay in terminals can also be predicted and eliminated by rescheduling departure time of targeted buses.

In order to better understand the impact of various elements on fuel consumption of vehicles, two mesoscopic fuel consumption prediction models were built to compare the performance between the nonlinear fitting method and neural networks and to investigate the influence of speed and acceleration on the calculation results of fuel consumption factor based on Vehicle Energy Dataset, which were collected from 383 personal cars in Ann Arbor, Michigan, USA. The time-snipping based segregation method was used to divide vehicle data into smaller time-snipping with fixed time intervals. To obtain more detailed parametric analysis, the effects of speed and acceleration on fuel consumption factors have been evaluated under different speed ranges, respectively. The results show that the performance of neural network is better than nonlinear fitting method and acceleration has a greater impact on fuel consumption factor at low speed compared with high speed.

Passenger flow prediction is of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely, Bi-directional Long Short-Term Memory with Attention Mechanism (Bi-LSTM-AT) is proposed to predict transit passenger flow. We utilize the Bi-LSTM structure with attention mechanism to capture the spatiotemporal features, meanwhile, taking into account external factors that affect passenger choices. We conducted an experiment using field data collected at Urumqi, China. The prediction results show an averaged absolute error (MAE) as low as 5.75, which demonstrated the feasibility of applying Bi-LSTM-AT in transit passenger flow forecasting.

This paper originates from the objective to verify the effectiveness of the K-means clustering algorithm in dealing with large sample data deviation and better describes the characteristics of highway traffic flow. The data was downloaded from the open source data of traffic flow detector of Whitemud Drive Highway in Canada. After data cleaning, K-means Clustering Algorithm was used to cluster large sample data to solve the problem of data deviation. Then the parameter calibration of Greenshields and Underwood traffic flow speed-density models according to the training set after clustering was conducted based on Least Square Method. Results of the two models are evaluated by means of statistics and probability theory. Finally, evaluation results show that the fitting effect of the Greenshields model is better than the Underwood model, and it is more suitable for decribing the traffic flow state of the highway in the study section.

Regarding the point merge system’s flight sequencing problem, there is no relevant research on the coordinated arrival and departure sequence under the multi-runway semi-mixed operation. This paper takes flight delays and air service time as the optimization goals. It establishes a coordinated scheduling optimization model for arrival and departure flights considering multiple runways’ semi-mixed operation. This model introduces a holding pattern based on the classic PMS so that the capacity is expanded without increasing the angle and length of the sequencing leg. Taking Beijing Capital Airport’s multi-runway semi-mixed operation as an example, the performance analysis of aircraft with different traffic densities and the sensitivity analysis of the objective function’s penalty coefficient is carried out through simulation experiments. Experimental results show that the algorithm can well solve the problem of collaborative sequencing optimization of the aircraft.

In this paper, we propose a Social Long Short-Term Memory (SLSTM) Neural Network for aircraft trajectory prediction. This model builds an LSTM network for each aircraft and uses a merging layer to merge the hidden states of its neighboring LSTMs. Then, it uses the merging results and trajectory information as its input for the next time step. Finally, the model is trained by maximizing the probability of real trajectory and evaluate the prediction effect. The experiment is conducted with the flight trajectory dataset over the San Francisco Bay Area in 2006. The evaluation shows that our model has the smallest error from 17 to 18 o’clock when the airspace’s flight trajectory density is the highest. The average horizontal error per point is about 282 meters, and the average vertical error per point is about 10 meters.

The application of video image recognition technology to identify and count moving targets in traffic scenes has become a research hotspot in the field of intelligent transportation. To accurately identify multiple moving targets in traffic scenes, this paper proposes a pedestrian target intelligent recognition method that combines neural networks and genetic algorithms. Using BP feedforward neural networks to achieve target classification and recognition, and uses a hierarchical genetic algorithm with global search capabilities to optimize the structure of neural network. It solves the BP algorithm shortcomings, such as easy to fall into local minimum, slow convergence, weak global search ability, difficult to determine network structure, etc., thereby improving the effectiveness and accuracy of pedestrian recognition. Results show the method can significantly distinguish pedestrians from other negative moving targets, accurately count the number of pedestrian targets in the entire traffic scene, and achieve better results in pedestrian recognition in dynamic scenes.

For road users, walking is the safest and greenest way to travel. However, with the rapid development of motorization, vehicle-pedestrian traffic accidents occur frequently. In response to this phenomenon, this paper is devoted to analyzing the factors contributing to the severity of vehicle-pedestrian accidents. Based on the data of vehicle-pedestrian accidents in Chicago, Illinois, a dataset of 10 influencing factors is constructed. Considering the severity of accident as the classification label, and considering the imbalanced distribution of samples with different labels, a 2-classification decision tree and an SVM are established to identify the severity of the accident. Then, through model comparison, it is found that the decision tree has the best classification performance, and the overall classification accuracy can reach more than 70%. Finally, nine eigenvalues were found that had a significant impact on the severity of the accident.

This research proposed a roundabout control for a single-lane roundabout when a connected and automated vehicle (CAV)’s market penetration rate is 100%. This system uses linear programming to optimize entering sequence for the global benefit (total delay minimization). A distributed consensus-based cruise control is designed for vehicles to enter the roundabout smoothly. The proposed roundabout control has the following features: it optimizes the movements of all vehicles for the global benefit. It is robust against unexpected situations, and also has an efficient cruise control to realize smooth coordination and can reduce abrupt braking under the premise of ensuring safety. A simulation-based evaluation proved that the proposed system is effective for mitigating performance degradation caused by heavy circulating flow and can improve roundabout capacity by about 50%. This system almost always avoids vehicles from stopping at the merging point. Delay benefits range from 91.5% to 97.6%. Fuel consumption benefits range from 0.6% to 57.5%. The variations are caused by different v/c ratios. The higher the v/c ratio, the greater the performance improvement.

Social media data, which contains a lot of traffic-related information, has gradually become a significant data source for transportation. The information describes and records the traffic conditions and events from multiple perspectives, which play an increasingly important role in solving traffic problems. This paper analyzes the characteristics of social transportation data and shows the processes and methods of data mining based on social media. It also categorizes social transportation data and summarizes analysis methods of social transportation data based on data types, and then summarizes the application of social media data in traffic information acquisition, traffic prediction, travel analysis, and public opinion analysis. Finally, the future work of applying social media data to social transportation is proposed based on previous research.

This study presents a road segmentation model with high accuracy and real-time performance. Road segmentation is a crucial problem in the application of autonomous vehicles, which requires real-time performance and accuracy. Nowadays, current models require high-resolution images to realize high accuracy performance, while the low-resolution input losses many details. However, high-resolution input creates much inference time and can’t meet the real-time requirement. To handle this problem, based on Efficient Residual Factorized ConvNet (ERFNet), we insert the feature fusion and enhance its upsampling blocks, which capture more of the roads edge information and achieve higher accuracy. This enhanced ERFNet decoder is designed with three main components: multi-scale feature fusion, revised factorized layers and dense upsampling convolutions. The proposed model shows impressing results on both inference time and segmentation accuracy, and this efficient model can be applied to autonomous vehicle systems.

The energy-saving low-frequency floating car data has increasingly attracted significantly attention in traffic management, but the low-frequency GPS data has adverse effects on map-matching in urban road networks. Therefore, this paper develops a new spatial-temporal path filter-based and route choice model aided map-matching (FCMM) algorithm that enhances the map-matching of low-frequency positioning data on an urban road map. We introduce filters based on spatial and temporal analyses, which consider real-time traffic status to eliminate unreasonable paths between adjacent candidate points, calculate the heuristic information, and establish the candidate graph. Considering the driver path preference, we further use the route choice model estimated from from real drive data to assess each candidate path. The algorithm was evaluated using ground truth data, and the results of the experiment show that the proposed FCMM algorithm outperforms the baseline methods in both effectiveness and efficiency under the condition of low sampling rates.

Partial-intended bus priority approaches take into account the characteristics of the continuous bus lane, and can use the pre-signal to provide a reasonable road space for the bus, which can improve the traffic efficiency of the bus, thereby reducing the per capita delay. However, setting the pre-signal requires a threshold condition to make the pre-signal evergreen when the traffic state does not satisfy the set condition. The sensitivity of these threshold conditions to the traffic state, and whether they can play their due role when the traffic changes matters. By setting the initial conditions, the research analyzes the constraint effect of the threshold condition when the traffic state changes. Through the example verification, the threshold can play its proper judgment role. After calculation, the example adopts the Partial-intended bus priority import channel to greatly reduce the total delay, and to some extent reduce the per capita delay.

Many Chinese airports have an unreasonable distribution of flight schedules, where runways or corridors are underutilized, which leads to delays. With the rapid growth of airports in recent years, it has gradually become standard that multiple adjacent airports share the same corridor. This means that it is possible to redistribute the corridor capacity to neighboring airport clusters. This paper proposes a single-airport flight slot optimization model that considers the public corridor of multiple adjacent airports and reduces unnecessary flight delays by allocating the remaining capacity of adjacent airports to the target airport. The model’s objective function minimizes the volume of adjusted flights by meeting capacity constraints to reduce the cost of optimization, while reducing delays. We apply this model to Tianjin Binhai International Airport and its neighboring airports. Simulation experiments show that this method can improve the feasibility of optimization and effectively reduce delays.

We improved the running rules of the traditional NS model by using the cellular automata, combined with the characteristics of Internet of Vehicles. We were also able to establish the single-lane cellular automata model. To increase the accuracy, we proposed a subdivided cellular automata model to study the impact of randomized moderation probability on traffic flow. Then we established a two-lane lane changing model to study the probability of lane changing. The time- space diagrams, flow-density basic diagrams and velocity-density basic diagrams under different densities are generated through the computer simulation. The results show that, compared with traditional environment, Internet of Vehicles improves vehicle speed and traffic flow under the same density conditions. Also, connected vehicles are more likely to change lanes when the road density is high. Thus transparency of vehicle information can alleviate traffic congestion and improve road capacity.

Vehicle trajectories clustering plays an increasingly essential role in understanding urban traffic patterns. In order to improve the clustering effect, we propose a novel clustering method integrated with multidimensional trajectory information based on ST-OPTICS clustering algorithm. This density-based algorithm utilizes spatial, temporal, road segment and direction angle information to form clusters of varying density based on spatial and temporal closeness. Furthermore, we utilize DTW to build a similarity model to measure the similarity between vehicle trajectories. Then we conduct experiments on a large-scale vehicle trajectory dataset consisting of 2172 trajectories collected from the GPS traces nearby Beijing Olympic Parks. Compared with four general clustering frameworks: DBSCAN, ST-DBSCAN, OPTICS and ST-OPTICS, we demonstrate that our method performs better than other clustering methods by evaluated on two internal cluster validity measures. Finally, we explore route choosing strategy according to the travel time of different trajectories and discover some abnormal trajectories.

Human mobility exploration through data mining gains many benefits from massive digital data sources. Geolocation data of mobile phones involves users’ spatio-temporal geographic information, but it does not include explicit labels of activities. This paper investigates individual activity patterns based on one-month mobile phone tracking data in the Paris region of France. The semi-definitive activity labels, including the primary anchor places and secondary activity places, are first extracted. The criteria of the cumulative presence duration and visiting frequency in activity locations over the study period are used to identify these places. To find individual activity patterns, characteristics such as activity frequency and activity duration related to the activity places are then investigated. Individual neighbor activity space is also measured around identified home and work places. Based on the individual activity features, we statistically analyze a set of activity patterns for all samples in our case study.

Identification and location tracking of drivers and vehicles can be effectively performed by utilizing license plate recognition data and Wi-Fi hotspot data. However, matching collected vehicles and vehicle driver data is challenging because the process has to cope with multiple data sources. In response to this problem, we propose an association analysis algorithm to efficiently solve the Person-Vehicle matching problem in a parallel manner. Experiments in Yunnan Province show that the method effectively solves the problem of matching drivers and vehicles, and has achieved good results. The research provides valuable insights on violation identification of vehicles.

Traffic flow forecasting is an important issue for traffic planning, design and management. In this study, we propose a multimodal deep learning approach for short-term traffic flow forecasting, which can jointly and adaptively learn the variation regulation of long temporal trend, seasonality, and residual of traffic flow produced by multi-dimensional decomposition. One-dimensional Convolutional Neural Networks (1D CNN) and Bi-directional Long Sort-Term Memory (Bi-LSTM) are combined with the attention mechanism. The former is to capture the local trend features of residual and the latter is to capture the temporal regulation of trend and seasonality. By introducing an attention mechanism, the strongly correlated historical information is connected for data fusion of the final prediction. Compared with SARIMA and LSTM, the experimental results indicate the proposed multimodal deep learning model with the temporal rolling strategy is capable of dealing with nonlinear traffic flow forecasting with satisfying accuracy and effectiveness.

Radars operate well to detect objects at a long-range under different weather and lighting conditions. They are more robust compared to LIDAR, visual system, GPS and other proprioceptive sensors. Using the FMCW radar scan as an input, we first extract landmarks from noise measurements with minimal false positives and avoiding the detection of ghost objects. To estimate the radar’s relative motion, we associate with different landmarks corresponding to the same location at different times. With the motion information and radar measurements, we generate an occupancy grid map based on the inverse sensor model fit with radar’s characters. We tested our method on the Oxford Radar RobotCar Dataset, and the results show that the map contours are sharp and distinct and our method can work well alone when other sensors become unreliable. Our grid map is also suitable for self-localization tasks and precise enough for ego-motion estimation.

To achieve the coordinated control of multiple intersections in a large-scale urban road network, we needed a method to study critical intersections’ global correlation characteristics. First, the Real-coded Accelerating Genetic Algorithm Projection Pursuit Classification (RAGA-PPC) was used to solve and evaluate the critical degree of intersections. Second, the k-means clustering algorithm was applied to intersections critical degree to divide the critical intersections and non-critical intersections. Third, a new approach of calculating the critical intersections’ correlation degree based on the improved FP-Growth algorithm, which can mine frequent itemsets of intersections from vehicle trajectories, was applied to analyze the global correlation characteristics of the selected critical intersection. Finally, Didi GAIA Dataset was selected for critical intersections identification and correlation analysis. The experimental results show that the correlation between critical intersections tends to decrease with global distance and varies with time.

The rapid development of artificial intelligence provides a new way for the research of transportation systems. Aiming at the problems of short-term traffic flow prediction such as lagging, insufficient time variable characteristics extraction, and low prediction accuracy, this paper uses the correlation of highway traffic flow in time as the basis to extract 4 types of variables closely related to time, and establish 6 Long-Short-Term Memory (LSTM) models respectively. The results show that a combination model that simultaneously considers multiple time variables can effectively reduce the lag in time series prediction. In addition, we establish two comparison models. The results show that the selected variables have both temporal characteristics and non-temporal characteristics. Capturing these characteristics can help improve the accuracy of the model. Finally, the Random Forest (RF) algorithm is used to rank the importance of variables, which further shows that the combined model has a certain feasibility.

The objective of the study is to explore the temperature field of the concrete cable-stayed bridge in the natural environment and the influence of the temperature effect on the displacement of the top tower. Based on the use of finite element softwares, which are TaiTherm, HyperMesh and Ansys, the simulation of the displacement of tower caused by temperature effect is well done and the change of the temperature field of the tower and the displacement track of the top of the tower are obtained. Results show that the temperature curve of the bridge tower is similar to a sinusoid with a period of 24h; the temperature change along the direction of tower height is small; the simulated displacement track of the top tower is roughly coincident with the measured trajectory; and the rotation angle and latitude of the bridge tower will affect the displacement of the top of the tower.

The surge of connected and autonomous vehicles has aroused rising attention from both academia and industrial world. In this context, this article introduces recent new applications concerning traffic control and how those traditional applications can be subverted by the emergence of those new-type vehicles. This article summarizes relevant study mainly in three aspects: traffic status perception, traffic control and evaluation.

In order to obtain the characteristics of traffic congestion in large Chinese cities, this paper proposes to conduct research based on the basic data of Traffic Performance Index (TPI) in Beijing. First, by introducing the concept and characteristics of TPI, two different methods of TPI calculation are compared. Next, according to the sensitivity of the response to congestion change, the method based on severely congested miles ratio is discovered to be more suitable for calculating the TPI of Beijing road network. Finally, the calculation method of TPI in Beijing is introduced through practical cases. By analyzing and comparing the TPI curves of different dates, it is proved that the characteristics of traffic congestion patterns on different dates are different.

In the CBTC system, the detection and precise positioning of train speed is the guarantee for the safe and efficient operation of the train. Therefore, an in-depth study of the speed and position detection scheme of urban rail transit can more specifically find the existing problems and reduce the measurement errors caused by the problems. Therefore, this paper makes an error analysis of the current speed measurement positioning method in urban rail transit combined positioning and hopes to make algorithmic compensation for this error to improve positioning accuracy. In addition, this paper compares the speed measurement errors of urban rail transit and high-speed trains, combined with the compensation scheme of high-speed railway speed measurement positioning errors. According to the special environment of urban rail transit operation, it proposes an improved method that is more suitable for the actual situation of subway trains.

Traffic flow prediction is widely used in travel decision making, traffic control, roadway system planning, business sectors, and government agencies. ARX models have proved to be highly effective and versatile. In this research, we investigated the applications of ARX models in prediction for real traffic flow in New York City. The ARX models were constructed by linear/polynomial or neural networks. Comparative studies were carried out based on the results by the efficiency, accuracy, and training computational demand of the algorithms.

Aiming at the random and non-linear features of short-term traffic flows in urban road networks, the research on spatiotemporal correlations of short-term traffic flows can demonstrate the entire evolution process of traffic congestion life cycles. Thus, the congestion path analysis algorithm is proposed to analyze spatiotemporal correlation of traffic state in urban road networks. Firstly, the existing Pearson correlation analysis method is improved by separating floating car data and extracting the trends and detail vectors of traffic flows, and the generalized least squares method makes the above method closer to the evolution of road traffic conditions. Secondly, based on the characteristics of traffic state classification, the Kendall coefficient is introduced to study the evolution of road traffic flow states. And finally, a high-correlation path search method based on fusion coefficients is proposed to provide method supports for traffic congestion predictions.

Nowadays, the phenomenon of congestion in rail transit is becoming more and more severe which need effective passenger inducement measures to alleviate this phenomenon. For solving the problem of precise estimation of passengers in crowded conditions, this article studies from the basis of theories and focuses on interval full load rate. First, a model was designed according to the interval full load rate and the number of passenger-controlled stations to calculate the rail traffic congestion degree. For getting the most accurate prediction results of the congestion degree, Autoregressive Integrated Moving Average model (ARIMA) and Prophet are compared to predict the interval full load rate. Then a simple model was designed to identify the Space-time range of congestion based on full load rate.

In recent years, received signal strength (RSS) based ranging method has become an important positioning technique for intelligent transportation systems due to its low cost. RSS based ranging using multiple frequency bands has attracted interest to reduce the influence of the RSS variation. The Log-distance path loss model is widely applied where the path loss exponent is usually considered as frequency independent. In this paper, a channel measurement based investigation reveals that the path loss exponent slightly depends on frequency, which results in the model mismatch for ranging methods using multiple bands. To overcome this mismatch, a simple RSS based ranging enhancement method is proposed. RSS measurements at different frequency bands are used to estimate the distance by considering frequency dependent path loss exponent. Furthermore, the covariance matrix of shadow fading factor at multiple frequency bands is incorporated. Measurement based evaluation shows the validity and outperformance of the proposed method.

In the traditional signal control design method, cycle length is calculated by Webster’s (1957) approximate optimum cycle length formula and each phase’s green time is distributed by the traffic demand rate ratio. This study develops a nonlinear programming model aimed at minimizing the average delay by calculating the delay using the traditional signal control calculation method. To verify the model, the open source traffic simulation tool SUMO which performs micro-traffic simulations, is used to simulate traffic at a four-leg intersection with signal under several assumed traffic demand scenarios. The results of applying the traditional signal control design method and the method developed in this study are compared. As a result, the method proposed in this study has a longer cycle time but shorter average delay time and shorter coefficient of variation of delay time than the traditional method for scenarios 1-3, which have higher traffic demand than the remaining scenario.

To solve the problem of congestion at intersections, an optimization method of intersection signal timing based on traffic flow forecasting is put forward. Firstly, a Principal Component Analysis - Long-Short Term Memory (PCA-LSTM) model is established to predict the traffic flow at the intersection based on intersection data from Huangshan Road-Science Avenue in Hefei. The predicted traffic flow is then taken as the input of Minimum Maximum - Multi-objective Particle Swarm Optimization (MNMX-MOPSO). Finally, the signal timing is optimized by considering the intersection capacity, average delay, and average number of stops. The results show that the REMS and MAE of the proposed forecasting model are smaller than those of the traditional forecasting model, and the traffic capacity after timing optimization is also better than the typical timing scheme. This study can improve the traffic efficiency of intersections and alleviate the problem of intersection congestion to a certain extent.

To solve the problem of frequent traffic congestion caused by the increase of traffic demand, this paper proposes a road network traffic operation evaluation method based on big traffic data. Based on the regional road network big traffic data, this paper studies and analyzes the evaluation indices of the road network traffic operation state, such as the proportion of serious congestion mileage, travel time and delay time in the road network. The fuzzy comprehensive evaluation method is used to evaluate the traffic operation state of the road network from different angles. The evaluation method proposed in this paper can accurately reflect the traffic operation state of the regional road network, which can not only provide reference for the daily travel route of travelers, but also provide strong data support for the overall planning of the regional road network in the future.

The response time is a critical indicator for Emergency Management Systems; it can be obtained through network analysis. This research uses ITS technology to collect, manipulate and analyze real-world data and information. Two datasets are utilized: SERPM based on estimation model and INRIX based on real-time data. The SERPM travel estimation model is weak in estimating under incident condition, while INRIX real-time data does not cover the study area along I-95 in Miami. This research examines the improvement of traffic emergency response with real-world and estimated traffic data; thus, specific incidents are selected, and four scenarios are created to make comparative analysis. ArcGIS is used to find the optimal response route from the integrated travel time network with SERPM and historical INRIX data. There is little difference in response time between historical and real-time INRIX data, but the response time for INRIX has a significant reduction when compared to SERPM.

Public transit systems play an important role in the alleviation of traffic congestion in urban road networks. The same vehicle type and a fixed departure timetable are usually applied to a bus route in the conventional public transit systems. They fail to cater to the time-varying travel demand or the diversified characteristics of transit passengers. To this end, this study proposes a demand-responsive public transit (DRPT) system consisting of a fixed bus route and demand-responsive stops with multiple vehicle types. The vehicle types of dispatched buses and the ride-matching schemes are optimized to serve transit passengers in real-time. Due to the non-convexity, Deep Q-Network (DQN), a reinforcement learning (RL) algorithm, is applied to the dynamic dispatching problem in the proposed DRPT system. The numerical studies validate the advantages of the proposed DRPT system and the RL-based dispatching algorithm.

Right-of-way image-based traffic sign recognition (TSR) is an important research field in intelligent transportation systems. Convolutional neural networks (CNNs) have made breakthroughs in TSR in recent years. However, the traditional convolution lacks invariance for affine transformations such as translation, scaling, shearing and rotation of symbols. To preserve spatial invariance of traffic signs, a Spatial Transformer-Convolutional Neural Network (ST-CNN) is proposed in this paper, where a Spatial Transformer Network (STN) is placed in front of different convolution modules. This method transforms images that are not easily segmented in the original image spatial into the feature spatial which is centered on the reference image and realizes the classification function. This paper uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset for training and testing. The performance of different STNs in the main network location is analyzed and the best model is selected. The accuracy in GTSRB is 99.36%.

Finding a shortest path between two locations on a stochastic time-dependent road network is an important constituent in vehicle guidance system. However, it is difficult for traditional heuristic algorithms to handle the complexity and stochasticity within the road network. In this paper, we model the stochastic time-dependent routing problem as a Markov decision process and utilize several reinforcement learning methods to solve this problem, such as Sarsa, Q-learning and Double Q-learning method. Sarsa method uses the actual Q-value for iteration instead of the maximum value function used by Q-Learning, while Double Q-learning utilizes two estimators to compute the value function, which can overcome the shortcoming of overestimation. Evaluated on ten stochastic time-dependent road networks, it can be concluded that Double Q-learning method outperforms other methods. Finally, the optimal paths acquired at different epochs are visualized to display the process of agent exploration.

“CBTC (Communication Based Train Control) + TBTC (Track-circuit Based Train Control)” is a dual-signal redundant train control system. The TBTC is the fallback mode, which is mainly implemented in the re-signaling program of the existing TBTC line. This study uses reliability block diagrams to quantitatively analyze the reliability of the dual-signal system and the time-colored Petri net of degraded operations to calculate the delay time caused by signal equipment failure leading to mode transition. These methods are applied to “CBTC + TBTC” re-signaling program of Shanghai Metro Line 2. The results show that the reliability of the new system is higher than that of the existing TBTC system and a single CBTC system, and the train delay caused by the fault leading to system fallback is minimized to 1 min. The analysis method also provides references for other multi-mode train control systems.

Intelligent Vehicle Infrastructure Cooperative Systems is a key research topic in the field of Intelligent Transportation Systems; traffic object perception based on cameras is one of its foundations. Due to the development of computer vision, single-camera traffic object tracking has implemented some advanced methods, but it is still inadequate in identity matching, especially because of more similar appearance. This study uses the background of multi-cameras and the DeepSORT algorithm as the basic framework to propose a vehicle identity matching algorithm based on the re-identification features determined by spatio-temporal information. The proposed method has been tested on a benchmark dataset of traffic video, achieving great performance and verifying its validity. Finally, this paper further discusses the advantages and disadvantages of the cross-camera vehicle tracking algorithm based on joint target matching of vehicle features and spatio-temporal information, contributing to the direction of future improvement of the algorithm.

Shield construction causes ground disturbance, which brings huge challenges to the safety of surrounding buildings. The maximum ground settlement is related to the ground loss rate, which can be controlled by managing the ground loss rate. However, in the shield construction, one should not only pay attention to the ground settlement, but also the deformation of surrounding buildings. Based on the engineering background of Shanghai Metro Line 13 crossing the maglev, the PLAXIS 2D model is established to calculate the ground and the maglev pile foundation settlements under different ground loss rates. Combined with the Peck formula, the modeling calculation results and the calculation results of the four formulas are compared, and the relationship between the ground loss rate and the settlement of ground and maglev pile foundation is statistically analyzed. The research presented in this article is intended to be used as a reference for subsequent research and engineering.

To alleviate traffic congestion and improve freeway operation efficiency, advanced technologies are proposed. On-ramp control is one of the most effective methods to realize the efficient management of the highway. This paper first describes several widely used management and control technologies and then focuses on the basic control principles, advantages, and disadvantages of the most attractive on-ramp control algorithms, such as local ramp control algorithm, cooperative ramp control algorithm, competitive ramp control algorithm, and integrated ramp control algorithm. Finally, based on summarizing the existing ramp control algorithms, the future research direction of the on-ramp control method is analyzed and prospected.

Due to the poor safety and low efficiency of the traditional road pothole detection method, a road pothole detection method based on a 3D laser point cloud is proposed. Based on the Pauta criterion, the abnormal points are detected, and the left or right normal points are used to correct the abnormal points. A quadratic parabolic arched pavement model is established, and a cross-section based on the dispersion of the point cloud data distribution of the cross section is examined for potholes. A method of twice fitting of arch pavement curve is proposed to extract pothole points using the data before and after fitting. Finally, the minimum area and volume detected at each data point is calculated to give the area and volume of the pothole. Experiments have proved that the method can accurately extract the pothole points, and the calculated pothole data has higher consistency compared with manual data.

Traffic prediction plays an important role in intelligent transportation systems. Accurate traffic forecasting can make for better traffic management and alleviate traffic problems, such as traffic congestion and traffic pollution. Graph data structure can well express the topology structure of traffic network, so graph model has more development space in the field of traffic prediction. The main purpose of this paper is to provide a comprehensive survey for the graph neural network in the field of traffic prediction. First, the graph model framework was divided into four categories, namely graph convolution networks, graph attention networks, graph auto-encoders and graph generative networks. Then, related literatures are introduced around the four frames. Finally, suggestions on the future development direction of the graph neural network are given.

The combined effect of network security threats and functional security threats leads to great hidden information security risks in the vehicle. When a vehicle is attacked and out of control, it will affect the vehicle network communication and traffic flow, resulting in traffic accidents. The key to ensuring the safety of intelligent transportation systems is to understand the spatio-temporal propagation characteristics of communication information distribution. Therefore, this paper focuses on the impact of vehicle-mounted information on the traffic network caused by the out-of-control transmission, and builds a dynamic model of vehicle networking network transmission and traffic flow coupling. For the real-time status of the traffic network in simulation experiments, it conducts in-depth research on traffic congestion caused by network security issues by traffic numerical indicators such as travel speed and waiting time.

A reasonable bus network is helpful to improve the quality of bus service and save travel time of travelers. The basis of bus network optimization is the diagnosis of the bus routes’ operation state and the identification of the lines to be optimized. In order to evaluate the operation quality of a bus network comprehensively and optimize the bus routes more accurately, eight multi-dimensional indicators and calculation models are proposed from the travelers’ perspective. A novel approach is proposed to diagnose bus routes operation state, and four types of bus lines that need to be optimized will be identified. Then, the identification thresholds of indicators are determined based on the actual bus operation data and passenger flow data. The proposed method can contribute to multi-dimensional evaluation and diagnosis analysis of bus routes operation and identify the bus routes to be optimized automatically based on large-scale public transport data.

Currently, with the development of connected and autonomous vehicles (CAVs), rich traffic flow information could be obtained easily to optimize trajectories. Existing studies that generally simplify trajectories as ideal curves or design a passing strategy for all vehicles at a constant speed fail to coordinate global trajectory and speed fluctuation. Therefore, a combined model aiming at optimizing the left-turn trajectory and approaching speed jointly for CAVs is proposed in this study. First, a rule-based classification method is proposed based on relevant indicators and three categories are subdivided. A changeable speed adjustment strategy, considering speed fluctuation is designed to generate the optimal strategy with minimum total time. Finally, the combined model is tested in three scenarios. Results show that delay of left-turn vehicles is optimized by 19.8%, and the proposed model could improve the operation efficiency of intersections.

Surface functional damage problems such as reflective cracks often occur during the use of asphalt pavements. This paper combines micro-surface study with rubber asphalt cape seal to analyze surface functional characteristics. In the preliminary design of the mixture ratio at the micro-surface, a 7mm sieve was added between the 4.75mm-9mm sieve holes to better control gradation of large particles, and based on the water-stripping resistance of the micro-surface, the gradation is optimized for design, and the optimal gradation and gradation range for water-peeling resistance of the micro-surface are given. Rubber asphalt cape seal was systematically studied on anti-slipping performance, slip resistance, and low-temperature crack resistance. Results show that, when rubber asphalt cape seal is compared with rubber asphalt gravel seal, properties of the layer are significantly improved. The rubber asphalt cape seal also has good resistance to reflective cracks, which can extend the service life of the road surface.

Analyzing and extracting interaction scenarios between vehicles and pedestrians (V2P) is of great significance for road safety. This research proposes a method of V2P data processing and scenario extraction based on naturalistic driving data of connected vehicles. By extracting real vehicle motion parameters, pedestrian motion parameters, and radar data from the Safety Pilot Model Deployment (SPMD), the V2P scenarios for typical and dangerous situations are extracted and analyzed. First, we use the threshold method combined with manual verification to identify dangerous events from the data set. Then, statistics of partial dynamic features of vehicles are chosen to be the parameters of the V2P scenarios and the Random Forest Model is used to filter out the important features as the final input of the clustering algorithm. Finally, the four dangerous and six typical V2P scenarios are extracted and analyzed. These scenarios could benefit design and testing for cooperative V2P safety applications.

Expressway section flow estimation is of great significance for specifying traffic management measures and guiding public travel. In view of the sparse distribution of highway traffic detection equipment, it is difficult to collect traffic parameters. Therefore, in the paper, the flow transfer coefficient between toll stations is used to reflect the spatio-temporal variation in the flow of the expressway network. The average travel time of the road section is estimated to restore the vehicle’s operating state. Thirdly, an estimation method of cross-sectional flow is proposed based on data fusion. Finally, in order to further reduce the estimation error, the RBF neural network model is used to modify the estimated value of the cross-sectional flow and obtain the final flow estimation result. The experimental results show that the method proposed in the paper can effectively estimate the traffic flow of the main line section of the expressway, which has certain reliability and practicability.

Metastable critical point estimation is enjoying much attention as traffic problems worsen in cities. However, related research on metastable critical point estimation seldom employs travel data due to the lack of data and the neglect of the catastrophe characteristic. This problem can be solved with the massive application of Electronic Registration Identification of the motor vehicle (ERI), which is an emerging technology to identify a unique vehicle based on Radio Frequency Identification (RFID). This paper presents an ERI data-based framework for identification of a critical phase transition interval and estimation of metastable critical point considering the catastrophe characteristics of traffic flow. We utilize real data in the algorithms. Finally, the metastable critical traffic flow characteristic parameter with the various observation period is analyzed quantitatively based on the LSTM model, which can provide an important basis for early warning and control.

Train delay prediction can assist in the reasonable formulation of a train diagram, and provide the basis for the decision-making of train dispatchers. In this paper, a hybrid method combining convolution neural network (CNN) and long short-term memory network (LSTM) is proposed to predict train arrival delays. First, eight characteristics (e.g., train departure delay, train actual running time) affecting train arrival delay are selected as the initial input variables of the prediction model. Next, CNN extracts feature again based on eight features, and outputs 32 new features. Further, combined with the newly extracted features, the proposed prediction model is trained using LSTM. Finally, the prediction performance of the proposed CNN-LSTM prediction model is evaluated based on the real-world operation records of the Wuhan-Guangzhou high-speed railway. The case study results show that the proposed prediction model has a higher prediction accuracy and is better than deep neural networks and LSTM.

The statistical characteristics of surrounding vehicles’ motions may significantly affect the performance of autonomous vehicles (AV). Therefore, AV driving policy may not work in a new area, which limits the driving of AVs across regions. This paper proposes a means of extracting and detecting traffic scenario characteristics, using a Variational Autoencoder (VAE). VAE is an unsupervised learning method, which can reconstruct the traffic data using neural networks. This method uses a vehicle’s state transition date as input and extracts latent variables in two dimensions. The extracted hidden variables can represent the driving characteristics of the environment. The scenario characteristics detector relies on the similarity of the hidden variables, using KL-divergence. The method is tested by the NGSIM (Next Generation Simulation) and highD dataset. The VAE are trained on 100,000 sets of data in 10 minutes. The results indicate that this method can accurately distinguish between regions, and detect traffic scenarios.

Prediction of queue length of vehicles caused by abnormal events on the expressway can help traffic managers to implement effective control of expressway sections. A flow estimation method for the target cross section and a speed estimation method for the target scene, which analyze the impact of ramp flow on the main road flow, is proposed. By comparing the applicability of the queue length prediction model based on the classic theory, this paper selects the appropriate model as the basic model and improves it considering the characteristics of the target scene and the actual data conditions, so as to realize the effective prediction of the queue length of the target scene. The validity and stability of the model is verified by the actual data in the target road section.

Following the occurrence of abnormal events on expressways, the estimation of time is critical for the reasonable control of traffic. Due to the influence of the detection cycle of the detection equipment and the delay of the detection algorithm, the detection time of many events has a certain delay relative to real occurrence time. This paper estimates the occurrence time of abnormal events by combining microwave detector data, toll data and manually recorded data. In this paper, the travel time of the road section based on toll data is estimated. Then, the event detection algorithm for the large detection cycle is designed to get the detection time closer to the real occurrence time. Finally, the detection time is modified by travel time. The experimental results show that the time estimation method based on travel time correction is reasonable and effective.

Results from automated pavement inspection equipment often deviate from the pavement’s actual condition, which leads to misjudgment of the pavement’s technical condition. This study takes inspection data obtained by automated equipment commonly used on Shanghai highways as an example and proposes a data correction process that considers the pavement damage composition characteristics. Based on 10.8 km of automated equipment and manual comparative inspection testing, the study suggests a data correction algorithm based on the single damage index and establishes a PCI (Pavement Condition Index) calculation model based on automated inspection data that integrates block crack, alligator crack, line crack, and the interaction term. Finally, the proposed correction model is validated with actual pavement measurement data, and the results show that the model has good accuracy.

Intelligent transportation systems (ITS) have developed rapidly for urban road networks in recent years. Accurate and efficient short-term traffic flow speed prediction is key to the realization of ITS. Traffic flow data usually perform stochastic and nonlinear characteristics, making short-term forecasting of large-scale urban road networks challenging. To extract the spatial and temporal correlations between traffic flows, we propose a novel short-term speed forecasting of large-scale urban road network based on the deep learning algorithm Transformer used in the field of natural language processing. We test the model using a real floating car dataset collected on a large-scale urban road network. It is found that the Transformer model shows high prediction accuracy and efficiency performance and outperforms the benchmark models.

A comprehensive and accurate evaluation of takeover risk is key to improving the safety of automated vehicles. The research on takeover mainly focuses on the factors affecting takeover performance, and rarely involves the quantification of the risk of automated vehicles executing a takeover. This study considers the influence of physical object shape on field distribution, in order to improve the existing driving safety field. On this basis, the safety index of a takeover was established, and a risk evaluation method of takeover considering multiple risk sources was proposed. The experimental results show that the improved field strength distribution is more consistent with the actual situation, and it solves the problem of overestimating and underestimating risk. The proposed takeover risk evaluation method considering multiple risk sources has good rationality.

Detecting whether the driver’s hand is off the steering wheel and warning the driver can improve driving safety. However, due to the impact of the vehicle’s movement and the significant variation across driving environment, detecting the position of the driver’s hand is challenging. This paper presents a method of using sensor data of smartwatch and smartphone to detect whether the driver’s hand is on the steering wheel in real time. This method infers whether the driver’s hand is on the steering wheel based on the gravity signal and the vibration signal and whether the vehicle is turning. In addition, this paper employs a method which keeps updating the training dataset to adapt to dynamic changes during driving. The evaluation of actual driving scenarios shows that this method can achieve a detection accuracy of about 90%.

One way to correctly allocate road maintenance resources is to effectively evaluate the benefits of pavement maintenance work through continuous data accumulation and analysis and to implement suitable measures to repair or maintain pavements at the appropriate time. In this study, an improved evaluation model of the average treatment effect (ATE) of pavement maintenance work is proposed. Taking the actual maintenance data of highways in North China as an example, the study analyzes the maintenance effects of three widely used local treatments, including direct overlay, overlay after milling, and three-layer milling overhaul. The results show that the proposed ATE model can assess the effect of pavement maintenance measures more reliably, and the predicted results are close to the actual situation. The study also gives the applicability of the model through the maintenance ratio parameters, which improves the model’s portability.

Under conditions of Cooperative Vehicle Infrastructure System (CVIS), the roadside control unit can obtain real-time road traffic conditions and intersection signal phase information. It can guide vehicles to adjust their speed reasonably and perform active traffic signal control based on vehicle operating status information, so as to improve traffic efficiency at intersections. In this research, a set of speed guidance systems were designed and developed under the condition of CVIS, which comprehensively considered different road traffic conditions and different application stages, and used msp430 as the hardware platform. The intersection of Shuanglong Avenue and Jiyin Avenue in Nanjing City was used as the research object, and the secondary development for model simulation was accomplished through the COM interface of VISSIM. The simulation results showed that the travel time of a single vehicle and the overall delay at the intersection were both reduced as a result of vehicle speed guidance.

This paper took Xinyi Road exit of Hetan elevated expressway in Urumqi as an example, established a VISSIM simulation model, and combined simulation conflict analysis software (SSAM) to propose an optimization scheme for traffic signal timing on an auxiliary road, which can improve traffic safety and traffic efficiency. In this paper, a DEA/AHP two-stage model is used to quantitatively calculate the simulation results before and after the optimization of traffic signal timing on an auxiliary road based on three aspects: the number of vehicles arriving, the average delay time between road network and vehicles and the number of vehicle conflicts within the section. The research results show that the auxiliary signal control method proposed in this paper can significantly improve the traffic order and traffic safety in the connection area, and provide reference for the organization, management and control of the connection area between an urban elevated expressway and a conventional road.

The outbreak of COVID-19 in 2020 greatly impacted China’s transportation industry. This paper aims to analyze this impact and the indispensable role of public transport control measures in preventing the spread of the epidemic. The impact of SARS and COVID-19 was compared. Taking Hebei Province as an example, the impact of the epidemic on the public transport industry was analyzed. The information of Jincheng public transport network and bus IC card data were selected for analysis. The parameters of bus network density and bus line repetition coefficient were calculated. The number of bus departures of each line before and after Jincheng epidemic was counted. Based on the data of bus IC card, the dynamic network of passenger contact was constructed, and the passenger contact network diagram was drawn. The analysis results can inform and enable urban public transport departments to take control measures when public health emergencies occur.

COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government policies and guidelines, but is more related to people’s awareness of the pandemic, which is reflected by the search data from Google Trends. Our results show that it takes on average 14 days for the mobility patterns to adjust to the new situation. From the spatial perspective, we conduct a state-level network analysis and clustering using the mobility data from Multiscale Dynamic Human Mobility Flow Dataset. As a result, we find that 1) states in the same cluster have shorter geographical distances; 2) a 14-daydelay again is found between the time when the largest number of clusters appears and the peak of Coronavirus-related search queries on Google Trends; and 3) a major reduction in other network flow properties, namely degree, closeness, and betweenness, of all states from the week of March2 to the week of April 6 (the week of the largest number of clusters).

In 2020, the outbreak of COVID-19 pneumonia has had a great impact on China’s economic and social life. The construction and transportation industries have been greatly impacted and suffered from its high mobility. This paper studies the big data of migration between Xi’an and Chengdu from January 1, 2020 to March 15, 2020 and divides the epidemic situation into four stages according to the introduction of the elastic coefficient according to the development of the epidemic situation. In each stage, the elastic coefficient of index change is introduced in combination with the decreasing impact of epidemic prevention and control measures on transportation. Finally, a modified moving average method is formed, which is compared with the ordinary moving average method. The results show that the modified moving average method combined with Hadoop big data platform can improve the accuracy and efficiency of the intercity transportation flow prediction under the epidemic situation.

To ensure traffic safety, many related works have been done to avoid traveler injury during trips. However, new public health issues threaten traffic safety because travelers might get ill during trips. The more people infected by COVID-19, the more unsafe urban traffic becomes. This paper aims to verify whether COVID-19 has negative impacts on urban traffic recovery. Based on thirty Chinese cities’ data, robust fixed-effects (within) regression was adopted to analyze impacts with a linear regression method. The regression results suggest that Urban Traffic Activity Index (UTAI) was positively associated with UTAI itself with short-term effect, meaning that UTAI could recover by itself, and new confirmed cases (NC) were negatively associated with UTAI with long-term effect, meaning that NC would prevent UTAI recovery. The findings also suggest that it is better for city governments to eliminate outbreaks before restarting economies. Future directions include improving models, grouping cities, and expanding data.

The traditional construction technology of “beam first and cable later” for self-anchored suspension bridges affects the traffic under the bridge, making it difficult to meet the demands of modern bridge rapid construction. Therefore, this paper proposes a new type of temporary tower anchor self-erection technology. However, during the construction process, the stress mode of the tower will change, and the safety of the tower becomes the most important issue. Therefore, this paper takes a double-tower three-span self-anchored suspension bridge as the example background to discuss the influence rule of the structural parameters of the self-anchored suspension bridge on the stress of the tower. The influence law of various structural parameters on the cable tower stress is obtained, which provides a reference for future design and construction.

Both cement mortar and cement concrete torque and rotating speed tester were adopted to explore the impacts of different admixtures on the rheological properties of fresh concrete, and to investigate rheological parameters, which consist of yield stress and plastic viscosity, with different amounts and types of admixture. With the increasing dosage of different types of water-reducer, both yield stress and plastic viscosity of cement mortar decreased, as did the yield stress of fresh cement concrete, but the plastic viscosity increased. With the increasing dosage of different types of air-entraining agent, the yield stress of fresh cement mortar and concrete, with saponins solid entraining agent added, decreased, and the plastic viscosity increased; the yield stress and plastic viscosity of fresh cement mortar and concrete, with PL cement liquid entraining agent added, kept decreasing. These results provide relevant references for the application of admixtures into cement concrete pavement engineering.

The accelerated loading test is more and more favored by researchers because it can obtain the change trend of pavement structure and material performance in a relatively short period of time. This paper first introduces the experimental scheme adopted in this accelerated loading test. The dynamic response under specific parameters is obtained by constructing the field test road and embedding sensors in the pavement structure. Second, according to the measured data, the typical three-dimensional mechanical responses of different layers of asphalt pavement, base and subbase are compared and analyzed respectively, and effective conclusions are obtained. These conclusions can be used as reference for pavement design and maintenance.

Longitudinal uneven settlement has been noted throughout soft soil areas, which has an adverse effect on tunnel durability. A health degree for longitudinal deformation of shield tunnel structures should be proposed to provide timely judgment and a basis for maintenance departments’ decision-making. A detailed 3D finite element model of the shield tunnel is established. With this model, the relationship between stress of components of shield tunnel structure and longitudinal deformation, composed of the rotation and shear dislocation modes simultaneously, is explored. A strong positive correlation between the longitudinal deformation and the stress of longitudinal bolt and segment is found. Based on this relationship, a health degree of longitudinal deformation of shield tunnels is proposed, which is partially proved by the limit value of the Chinese standards. In comparison with the standards, more deformation form can be considered, which is more practical in the field.

Long-term settlement of metro tunnels due to repeated train loading in a four overlapping tunnel case is investigated based on the finite element method and the empirical method, and the influence from the net distance and the train speed is discussed in detail. The results show: (1) Uneven settlement is observed on the tunnels with a maximum settlement occurring at the intersection of upper and lower tunnel axes. (2) The range of the settlement through is within about 4 D (diameter of the tunnel) from the intersection points, beyond which dynamic amplification effect of the four tunnels is too small to cause uneven settlement. (3) The net distance of the upper and lower tunnels has a nonlinear effect on the uneven settlement that increases sharply with the decreasing of net distance. (4) However, the train speed has little influence on the uneven settlement within the common speed range in practice.

To reveal the mechanical properties of sandstone cement stabilized gravel, samples were prepared according to the principle of vertical vibration. The effects of rock type, cement content and aging on compressible strength and splitting strength were studied. The results show that sandstone origin and cement content will affect the mechanical properties of cement stabilized crushed stone base. The 4.0% cement content is the turning point of the curve of infinite compressible strength with healthy age; the softening coefficient of sandstone cement stabilized crushed stone increases first and then decreases with the increase of curing time; the mechanical properties of sandstone cement stabilized crushed stone from different origins meet the specification requirements, the basic layer could be applied.

Developing Y-shaped lines in China is helpful to solve the problems of low coverage and low utilization rate of rail transit lines in suburban areas. At present, most scholars consider the departure frequency and the choice of subway routing node separately to discuss and study, lack of research on combination effect. This paper combines the departure frequency and metro routing nodes to analyze the operation index changes of Y-shaped line. And Y-shaped line of Guangzhou city as the basic data. Departure frequency and train routing nodes are combined to analyze the operation index changes of Y-shaped line. The calculation results show that the combination of the two is helpful to improve the passenger waiting time and train running kilometers under different routing conditions. The collaborative optimization between them is realized.

In order to coordinate the relationship between supply and demand of urban rail transit, this paper established an optimization model of train operation density for single track, based on the theory of collaborative current limit. Besides, the model integrated with the objective function which made the sum of waiting time of inbound passengers and detained passengers shortest. And a series of constraints were set to ensure the effectivity of the model: passenger demand, station capacity, section conveying capacity, line capacity, train interval capacity. In addition, applying the model in a calculation example as a case study, results demonstrated that the optimal density of trains with coordinated passenger flow control on a single line can effectively alleviate the operating pressure of large passenger flow during peak hour.

The surface settlement while shield tunneling in clay is consistent of the instantaneous settlement and the consolidation settlement. The consolidation settlement develops with the dissipation of the excess pore water pressure after the shield tail leaves. Considering the excess pore water pressure dissipation, a theoretical method of predicting surface settlement is proposed in this study. The exponent parameter α in the method is determined with a field measurement of the excess pore water pressure. The method is verified by the monitoring data of a large diameter shield tunnel in soft clay, Hangzhou. The result shows that the prediction method has a good feasibility in predicting the surface settlement caused by shield tunneling. In addition, the assumption of parabolic distribution is suitable for the excess pore water pressure above the tunnel axis, and has a reference value for the control of the surface settlement in soft clay.

The analysis and prediction of regional comprehensive traffic passenger transport structure can deeply understand the change law of passenger transport structure and improve the level of passenger transport service. This paper puts forward three kinds of passenger transport structures: basic, medium and high-quality, and constructs the Markov chain passenger transport structure prediction model. Taking the comprehensive passenger transport structure of Shandong Province as an example, this paper uses Python to solve the passenger transport structure in the future years. The results show that the proportion of highway in passenger transport in Shandong Province is shifting to high-speed rail and civil aviation, the proportion of high-quality passenger transport structure is increasing year by year, and the proportion of water passenger transport is relatively stable, about 3%, which is in line with the actual development of comprehensive passenger transport in Shandong Province.

The construction of transportation infrastructure is not only the foundation of regional survival and development, but also the important foundation of realizing regional functional orientation and improving regional comprehensive competitiveness. The rational allocation of traffic land resources requires the prediction of the limit scale of transportation infrastructure. Through the combination of qualitative analysis, quantitative evaluation and empirical research, a theoretical calculation model for predicting the land scale of highway traffic infrastructure construction based on RR-ARIMA coupling model is proposed. Taking Shandong Province as an example, the total limit scale of regional highway traffic infrastructure from 2022 to 2025 is predicted, and it is concluded that the limit scale of highway infrastructure at the end of the 14th five-year plan is 79.99-91.58 ten thousand hectares.

The properties of soil have great influence on road engineering. Various laboratory tests are carried out to study the properties of soil, including the most representative triaxial test. However, laboratory tests generally focus on the acquisition of macro mechanical properties, which may not reflect the stress and movement of soil particles in the sample. In this paper, by using the discrete element method (DEM) and particle flow code (PFC) software, the triaxial test and slope stability problems are firstly modeled and analyzed, then the relationship between micro parameters and macro mechanical behavior is discussed and summarized. After that, the formation and development of shear zone is also observed in PFC, and the stability factor of side slope calculated based on strength reduction method is 3.56. Lastly, the methods of soil slope stability prediction by geotechnical laboratory test and DEM software are summarized.

The addition of Polymer or nano-particle generally improves the viscosity, rheological properties, and fatigue resistance of asphalt binder. However, the thermoelectric effect of nano-modified asphalt concrete has been barely investigated. In some regions, the surface temperature of asphalt pavement can reach 55°C, which is due to consistent solar radiation, whereas the base of road maintains a relatively stable temperature (i.e. 27°C to 33°C). The notable temperature gradient between the surface and base may be utilized to generate electrical power. Asphalt binder and concrete behave as insulators, thus carbon nanotube (CNT), a conductive nanomaterial, is probably a promising modifier. Various contents of CNTs were added into asphalt binder to examine the high-temperature characteristics, including penetration, ductility, and softening point. Rutting laboratory experiments were also carried out on modeled specimens to simulate field circumstances. Furthermore, an innovative thermoelectric effect experiment was conducted. The results indicate the addition of CNTs effectively improve the high temperature stability of modified asphalt concrete. The thermoelectric performance of modified asphalt concrete is considerable, with an electromotive force rate of 21.5 µV/°C.

Large deformation of rich water loess tunnels has been a serious problem for a long time. To analyze the causes of large deformation and give countermeasures, a model is established based on the Yulinzi tunnel in Gansu by the load-structure method. This model considers support forces including the friction force and bearing capacity. Calculation results show that the main causes for the large deformation are lack of systematic bolts, insufficient strength of foot steel pipe, and foundation softening caused by the accumulation of groundwater. To solve these problems, four kinds of countermeasures are proposed. Through the model calculation, increasing the diameter of the foot steel pipe is more efficient to control the deformation. The study shows that the model can provide the influence degree of each factor on the deformation of the tunnel. Moreover, the model helps find a way to control deformation and provides a practical guide for the construction of similar projects.

Rotating furnace slag (RFS) is a stable material. In this study, the performance of thin road overlays (SMA-10), in which natural fine aggregates and fillers were replaced with RFS, was investigated in detail. X-ray diffraction fluorescence (XRF) and the toxicity characteristic leachability procedure (TCLP) were employed to characterize the RFS. Based on the chemical properties of RFS, the performances of several SMA-10 mixes with different contents of RFS were evaluated. The results indicated that RFS is strongly alkaline, demonstrating that it has higher adherence with asphalt binders. In addition, RFS is environmentally friendly, due to low toxic leaching. For SMA-10 designed by Marshall, the fine aggregate characteristics of RFS could significantly reduce the optimal asphalt content. Meanwhile, by controlling size distribution, gradation curve, and limiting the RFS content to less than 40%. SMA-10 consisting of RFS aggregates and powders demonstrated superior resistance to cracking, deformation, and moisture-related damage.

We used multi-scale finite element model (FEM) to give better predictions of bridge mechanical behavior in field tests. A multi-scale FEM of the overall bridge was developed based on practical bridge engineering and its accuracy was verified by the traditional beam element model. The multi-scale FEM was divided into the unconcerned segment and concerned segment. The concerned segment of the bridge girder was built with the solid elements to improve analysis accuracy. The unconcerned was built with the beam elements to save on analysis costs. The comparisons among the FEM results and field test shown that the multi-scale FEM was especially suitable for the bridge field test. This type of model can give precise predictions on the stress distribution and flexural stiffness of the tested bridge.

We established a finite element model simulating the interlayer bonding conditions of asphalt pavement. Thirty-five values of friction coefficients from zero to 500 were used as input parameters to analyze the interlayer conditions’ influence on different layers’ shear stress distribution. We analyzed the factors affecting shear stress and examined the orthogonal design and variance analysis of those factors’ effect on shear stress. The result shows that the interlayer bonding conditions significantly change the shear stress distribution in the asphalt pavement. The value of the friction coefficient from 1 to 0 cannot represent the change of the interlayer bonding conditions from full to partial bonding. The value of the friction coefficient is redefined based on the shear stress distribution. The contact conditions between the base and the asphalt layer significantly impact the lower layer’s shear stress. Still, the contact conditions between asphalt layers affect the entire surface layer.

In this paper, we selected section “A” and “B” of corrugated steel culvert for field testing and numerical simulation based on the reconstruction and expansion project of Yan’an. The results show that in the process of soil filling, the shear strain of corrugated steel pipe culverts fluctuates with the change of the height of the pipe side and the overlying soil. The maximum tangential strain in the internal measurement is 503 με and the maximum tangential strain at the external measuring point is 680 με. The field measured results are in good agreement with the finite element results. The vertical earth pressure at the top of the pipe culvert is the largest, so the earth pressure on the top of the pipe should be taken as an important reference in the design and calculations of culvert planning.

Traditional methods for determining aggregate index properties are time-consuming and make it difficult to provide a quantitate evaluation of particle shape. With the advances in computing power and image analysis techniques, new algorithms and equipment have been developed to characterize the aggregate index properties. This paper provides a review on the most promising computer vision technology. Discussions on shortcomings of the methods and recommendations for future research are also provided.

According to the characteristics and requirements of high-speed maglev transportation, several auxiliary stopping areas and stopping points are set up along the track. In normal operation, the operation control system controls the maglev train to run continuously and stop at stations through stopping point stepping. In the event of an emergency or serious system equipment failure, in which case the system cannot continue operation, the operation control system will implement a forced stop to control the maglev train to safely stop at the current stopping point. Based on the study of the principle and mechanism of the auxiliary stopping area and stopping point stepping control, this paper focuses on the analysis of the forced stop operation scene for high-speed maglev transportation, and studies the trigger conditions and causes of the forced stop, the disposal process, and the stopping methods.

The study explored using hard shoulders for congestion caused by accidents. The shoulder could serve as managed lanes to alleviate congestion or allow emergency vehicles to pass. Results show that congestion would be much more severe with hard shoulders functioning as emergency lanes compared as additional lanes, while the efficiency of accident handling would be slightly better. Meanwhile, handling accidents promptly is critical for safety and for alleviating traffic congestion. For a comprehensive consideration, it is recommended that after rescue vehicles have arrived at the accident location, the hard shoulder could be used as an additional lane to alleviate the congestion. The mobility could be increased with the proper use of hard shoulders.

For high-speed maglev operation control system, train location is a safety-critical function. Whether these functions can be realized has a vital impact on the safe operation of the maglev train. This paper first analyzes the interface relationship between the operation control system and the vehicle location system, and then establishes safety processing modeling for the operation control system by means of the state diagram. In order to control the complexity, the safety processing model is divided into two levels, namely the location system state machine and the location channel state machine. Through the safety processing, the non-safe location information generated by the vehicle location system is processed as safety location information, such as position, speed and driving direction of a certain train.

Currently, estimation of vehicle speed in forensic videos is becoming increasingly important for analyzing traffic accidents. To improve the efficiency and scientific nature of this study, an analytical model is proposed. In this method, video code information is detected to calculate the time of video based on its frame rate and moving scale for calculating distance. Next, a distance interpolation method is established according to video status. Then, a velocity curve is created to analyze the braking deceleration of the vehicle. Finally, as a proof of concept, the proposed analytical model is implemented for simulation cases and actual traffic accidents. Experimental results demonstrate the feasibility of the method in terms of accurate estimation of vehicle speed. The results indicate an average relative error of less than 3%. This analytical model is highly versatile and can be used for in-depth investigations of traffic accidents.

The boom of dockless bike-sharing (DlBS) systems in China has restored bicycling and changed people’s daily travel modes, mostly by impacting walking and public transit systems. The impacts of DlBS on public transit could be either substitution or connection. However, how DlBS substitute or connect to public transit in practice and to what extent, these are still unanswered questions. In this paper we initiated a case study based on survey data in Shanghai, found five main ridership changes after the emergence of DlBS and employed a nested logit model to explore the affecting factors. It was observed that 29% of respondents using DlBS to connect public transit instead of walking, which results a 28% of walking time decrease averagely. About 12% respondents using DlBS as a substitution of bus, which results no travel time increase on average. The findings highlight that DlBS is effective to solving the first-last mile issue.

The duration depends on four stages of traffic accidents, including discovery/detection, response, disposal and recovery. Due to the limited data or resources, it is difficult to quantify the detection time. However, the detection time appears to be the most important component, since it is responsible for the first and the majority of the duration. The distribution mode of the detection time for highway traffic accidents is researched. Some factors and the discretization method are discussed to show the influence rules. Finally, an example of verification is carried out in Shaan’xi province. The proposed discretization method is effective as the calculated result.

This paper uses traffic conflict technology and conflict indicators to explore non-motorized vehicle-pedestrian conflict at bus stops. Three bus stations, along with the motorized and non-motorized vehicle isolation strip in Xi’an, are taken as examples. Traffic Analyzer George 2.1 was used to extract relevant data such as velocity, acceleration, and trajectory of both parties in the traffic conflict. This paper analyzed the trajectory characteristics of non-motorized vehicle lateral offset under the influence of pedestrians, and the severity of conflict under different avoidance forms. The conflict distance and Time to Collision (TTC) were selected as evaluation indicators, and the k-means cluster analysis method and cumulative frequency analysis method were used to respectively determine appropriate thresholds for judging the severity of non-motorized vehicle-pedestrian conflict at bus stops. Results show that: 0 < TTC≤1.0s means serious conflict, 1.0s < TTC≤1.9s means general conflict, and TTC≥1.9s is not considered to constitute a conflict.

To study the differences in psychological characteristics between elderly drivers and young and middle-aged drivers in risk situations, and to determine the decline of psychological characteristics of elderly drivers, five experimental scenarios with different risk levels were designed. Drivers’ heart rate variability frequency and time domain data were extracted by physiological instruments, and the two groups’ HRV indicators were compared and analyzed. Results show that the frequency domain index LF and LF/HF of the two groups both increase with the rise of risk grade, while the time domain index SDNN of HRV decreases with the rise of risk grade. In addition, the SDNN index of elderly drivers declined by 29.14%, indicating that the operating load of elderly drivers is relatively higher in risk scenarios. Compared with young and middle-aged drivers, elderly drivers require greater mental effort to deal with risks, which increases the probability of accidents among elderly drivers.

Information guidance within a transportation hub is helpful for the pedestrian evacuation during an emergency. In relation to the cellular automata (CA), this paper created a pedestrian evacuation model to simulate the process of the pedestrian movement with information guidance. The parameters of distance, density, and information guidance are provided to identify the pedestrian movement directions. The influences of parameters under two different pedestrians’ view conditions are calibrated. By simulation, the evacuation time in the various densities with or without evacuation signs is compared. Then the impact of the information guidance is analyzed. Provided with information guidance, the pedestrians are less likely to wander and know better where to go. In conclusion, information guidance reduces the evacuation time and makes the pedestrian evacuation more organized, improving the system security. The simulation has theoretical and practical significance for the hub planning and design.

To predict the development trend of traffic accidents and improve the prediction accuracy of macro indicators of traffic accidents, three direct indicators of road traffic accidents were taken as output variables and six macro indicators were taken as input variables to establish the NARX model. The model was trained and fit based on China’s data from 2001 to 2016, the modeling results were used to predict three direct indicators of traffic accidents in 2017 and 2018. To validate the performance of the NARX model, MLR model, BPNN model and GRNN model were also used as comparative benchmarks. The models were compared by selecting mean square error (MSE), Theil IC (TIC), mean absolute error (MAE) and mean absolute percentage error (MAPE) as the error analysis indexes. The results show the NARX model accuracy is better than the other three contrast models in the aspect of traffic accident macro index prediction.

The emergence of new energy vehicles, especially electric vehicles (EV), has brought many development prospects. Power cuts in private electric cars can cause drivers to panic over long trips. Therefore, this paper proposes a charging path planning method for EV considering the influence of road condition information. This paper first analyzes the influence of energy consumption, vehicle delay estimation and traffic impedance under the control of different phase signals. On this basis, a path planning model is established to study the charging path planning of electric vehicles under the influence of road condition information. Finally, the feasibility of the model is verified by comparing different algorithms and different path selection results with and without traffic signal control, which provides a theoretical basis for the research of path intelligence guidance system.

The real-time prediction of passenger flow in subway station passage sections is of great significance for operators to obtain real-time data support, ensure the safety of passenger flow in the station, and realize intelligent passenger transportation management. This paper adopts a real-time passenger flow forecasting model based on wavelet variable weight combination. First, the trend and volatility of passenger flow data is decomposed and reconstructed by wavelet analysis method; Next, two basic prediction models of SVM and RBF to make predictions are used, and add wavelet packet analysis method; last, the forecast results after the wavelet analysis obtained in the previous step are combined with variable weights to compare the prediction effects before and after the variable weight combination. The results show that the wavelet variable weight combination model, using wavelet analysis, SVM model and RBF model not only improves the prediction accuracy, but also improves the prediction stability.

This paper introduces two types of preference, indifference threshold and induced information trust, establishes an update model of induced information trust, analyzes the feedback mechanism of individual preferences in path choice behavior, and establishes a basis based on individual preferences path selection learning behavior model. The traffic guidance strategy based on individual preference is designed, and the traffic system efficiency of the guidance strategy is studied with MATLAB simulation. Under the two types of traffic guidance strategies based on individual preferences, the four dimensions of traffic flow of each road segment, total travel time of the road network, the number of people on each path, and the results of the driver’s path selection, the simulation days increase and the indifference threshold increases. The results show that the traffic guidance strategy of individual preference can better optimize the allocation of traffic resources, evenly distribute traffic pressure, and improve travel efficiency.

This paper proposes a design method of color non-motor vehicle waiting area based on slow transportation priority in order to alleviate the traffic conflicts between motor vehicles and non-motor vehicles at intersections, ensure the safety of traffic participants, and improve the operating efficiency of intersections. Taking the intersection of Xincheng 2nd Road and Bocheng 3rd Road in Boxing County, Binzhou City, Shandong Province as an example, the paper calculates the traffic capacity before and after the setting of the waiting area and uses VISSIM simulation to obtain queuing delays. The results show that after the non-motor vehicle waiting area is set up, the capacity of non-motor vehicle of the East and West entrances at the intersection has been improved, and the queuing delay has been significantly reduced, especially for the East entrance.

The safety risk assessment of expressway tunnel operations, which is a multi-factor complex system evaluation problem, can be considered as a critical process of expressway operation management. From the perspective of the operating company, the risk evaluation index system of expressway tunnel operations can be established through actual operation conditions, and the objective weight of the evaluation index can be calculated through the CRITIC weighting method, after that, the digital characteristics of the risk index could be obtained through the cloud model. The comprehensive certainty value that belongs to each risk level could be gained, and the result should be determined according to the principle of maximum value. Finally this model could be applied to the overall risk assessment of the tunnel, and it is shown that the risk assessment result of expressway tunnel obtained by this model is similar to the actual operation conditions of expressway tunnels.

Recently, the phenomenon of large parking gaps and low utilization of parking spaces is common. Based on the analysis of shared berth demand and supply, this study establishes a behavior model of shared berth selection. According to a two-day field survey on the occupied parking spaces, the number of vehicles arriving and leaving the West courtyard of Lijiacun, this paper analyzes the personal attributes, daily parking attributes, sharing attributes, shared security and economic attributes and convenient and flexible perception attributes of parking users. Then, the behavior model of berth sharing selection is established by using SEM-logit combination model, and the characteristics of travelers’ sharing demand are analyzed. In the aspect of shared parking space supply, BP neural network and SVM are used to predict the supply capacity of regional parking lot. The results show that SVM has higher precision prediction and can provide data support for shared parking service platform.

Ride-haling services are becoming popular in cities, especially for young people. Many studies have investigated ride-hailing usage behavior for the general population, few studies focus on the university population (key users). This paper conducts an online survey to investigate students’ usage behavior of ride-hailing services in Chinese university communities. Descriptive analysis and discrete choice models (DCM) are performed to understand usage frequency and preference of service types. Descriptive analysis reveals that over 60% of students use ride-hailing 1-3 times per month and nearly 45% of students choose express service for traveling. The important factors impacting usage frequency include occupation, monthly expenses, car ownership, car-sharing membership, and public transport membership. Similarly, commuting time, usage frequency, time of day, travel time, status, age, monthly expense, as well as car ownership influence the preference of service types. The findings provide insights for optimizing the operation of ride-hailing services in cities.

Vehicle anti-collision warning systems within the V2V environment can enable drivers to obtain the driving and pavement condition information of other vehicles beyond visual range. The safety distance model is the core of the early warning system. In this paper, a more reasonable safety distance model is established according to the different states of the leading vehicle (stationary, uniform speed, uniform acceleration and uniform deceleration), considering the driver factors, road conditions, information delay and positioning error. The paper builds a vehicle anti-collision warning system for simulation test, the simulation results show that without the influence of other factors, the safety distance required under dry asphalt pavement is the lowest. In the performance test of warning system, the false alarm rate is kept at a relatively low level during uniform speed, uniform acceleration and uniform deceleration. The reliability of the system meets the requirements.

The imbalance between supply and demand causes low operational efficiency for Transportation Network Company (TNC) services. Demand management is an efficient way to re-distribute requests in space and time dimensions, and eventually, enhance service operating efficiency. Understanding and influencing travel behavior is a significant challenge in demand management. This paper investigates users’ attitudes toward incentive strategies for TNC services in China: 1) share trips with others, 2) extra walk to a pick-up or drop-off location, 3) request rides in advance. Survey data was collected from university communities in Suzhou, China. Descriptive analysis and binary logit models are developed to understand users’ preferences over these strategies. Results highlight a high acceptance of these incentive strategies. Common factors impacting the acceptance of these strategies include commuting mode, commuting time, monthly expense, ownerships of cars/e-bicycles, and public transport membership. The findings provide insights into the planning and management of TNC services in cities.

The safety risks in the operation process of the long river-crossing tunnels were identified systematically and comprehensively and the risk assessment index system was established by categorizing, merging and integrating the factors of people, vehicles, tunnels, environment and management. Combined with the safety science and system theory, the complex safety risk factors were refined. Then the analytic hierarchy process was used to determine the weight of the index, and the gray correlation theory was used to analyze and calculate the value of the index. Some reference and ideas can be provided for the operation safety risk management of the long river-crossing tunnels.

Driving behavior is an essential factor affecting road traffic safety. To improve road traffic safety, studying the key influencing factors of unsafe driving behavior and its influence degree by using the results of the questionnaire survey. From driving behavior theory, unsafe driving behavior is divided into non-compliance, misoperation, and negligence. Based on questionnaire data, determine the correlation test impact factors, then establish a multivariate disordered Logistic regression model based on classification. Analyzing the regression model, the key influencing factors and their influence degree is obtained. Results show gender, driving style, single driving time have significant influence on non-compliant driving behavior; gender, driving experience, driving style, psychological quality of driving, and single driving time have a significant impact on driving behavior of misoperation, while female and lack of driving experience are more likely to cause frequent operation errors. Besides education background, age and driving time have significant effects on negligent driving.

The resilience of a traffic network is critical for emergency relief in a public emergency. This paper evaluates the resilience of the high-speed railway network in the Yangtze River Delta based on the independent path. The dominance and vulnerability of nodes and edges are analyzed by node blockage and edge disruption simulation. The results show that the resilience of a node is basically consistent with the number of independent paths. Hangzhou, Nanjing, and Shanghai are the most dominant nodes. Their blockage will reduce the resilience of the traffic network by 55.02%, 48.41% and 41.86%, respectively. Meanwhile, their blockage will obviously reduce the resilience of vulnerable nodes such as Lianyungang and Huzhou. The most dominant edge is from Shanghai to Hangzhou, its disruption will reduce the network resilience by 26.18%. Hangzhou and Hefei, as cities with high dominance and high vulnerability, need to formulate contingency plans to reduce their vulnerability.

Road intersections have become an important part of the urban traffic system and the design quality of urban road intersections (URIs) will directly affect traffic conditions. Many evaluation methods have been established to guide the construction of urban road intersections. However, current analysis frameworks do not depict human activities in urban road space. This study proposed a sequence framework without additional investigation and experiments with several deep learning models to extract all the urban road analysis features which includes human scale variables. Three categories of URI are classified in the paper. Besides, a multivariate linear model (ML) is built to find the relationship between the possible variables and the URI distribution space. ML shows that geometric design conditions, traffic subsidiary facilities and human activities have positive effects on a good URI distribution. According to the model result, the paper proposed several suggestions to design a good URI.

Existing literature suggests that accident rates in road tunnels are lower than those in open roads, but the accidents are considerably more severe and the rear-end collision is the most prevalent crash type in road tunnels. Compared with open roads, the driving behavior is affected by many external factors in tunnels, whereas the existing quantitative indicators of risk perception models are mainly generally based on information of the vehicle’s state. In this study, we develop an improved driving safety field to describe the influence of driving risk caused by drivers, vehicles, roads, and changed environments on driving behavior. The improved driving safety field includes the following three parts: (i) potential field, which is by nonmoving objects on the roads, such as no-moving vehicles and speed limit signs; (ii) kinetic field, which is determined by the moving vehicles on roads; (iii) environment field, which is determined by the environmental factors, such as illuminance level and illuminance change rate. In addition, the field force acting on the vehicle is used to represent the vehicle’s current risk. This research may help to develop control and management schemes to decrease the driving risk in tunnels.

Internal mechanisms affect the usage intention (UI) of urban residents to car sharing. Using traffic travelers as the research object, and based on the theory of planned behavior, four driving experience factors including charging rationality (CR), ease of use (EU), travel comfort (TC) and perceived risk (PR) are considered. A structural equation model is constructed of urban residents’ usage intention (UI) of car sharing. Through a questionnaire survey, the quantitative relationship between seven psychological variables and UI of car sharing is analyzed. Clustering characteristics are studied according types of cities where travelers live and whether they own private cars. Results indicate that the impact of CR on attitude (AT) is high. Attitude, subjective norms (SN), perceived behavioral control (PBC) and PR will have different degrees of influence on UI, and SN is the key factor. The research provides a reference for government agencies and car sharing companies to make policies.

Unsafe driving behavior of bus drivers is one of the main causes of passenger traffic accidents. To regulate the driving behavior, a detection method of steering wheel operation behavior of bus drivers based on hand feature extraction is proposed. Firstly, the Adaboost target hand classifier is constructed by a multi-feature cascading detection method, and the driver’s target hand detection is carried out in the region of interest of the steering wheel to obtain the number and position information of target hand. Then, the position of the steering wheel in the detection image is calibrated to identify the position relationship between the target hand and the steering wheel. Finally, the classification rules for unsafe driving operations of bus drivers are formulated. The experimental results show that the average detection accuracy is 94.0%, the average detection speed is 30ms/frame. The method has good real-time performance and robustness.

In this study, the time track of drivers’ behavior characteristics is visualized to explore the safety-ecological (SAF-ECO) characteristics of individual drivers. This study collected micro-driving behavior data by the on-board diagnostics (OBD) unit and divided the driving population into four categories based on the calculation of the “SAF-ECO” index. In terms of the elements of node and time axis, a driving behavior characteristic graph is established based on micro driving behavior indicators. Finally, the probability distribution is used for further verification and analysis. The results show that the analysis results of driving behavior characteristic graphs are consistent with the classification of “SAF-ECO”. The graph directly shows the changing process of individual drivers’ driving behaviors. A reasonable coupling characteristic description of SAF-ECO’s driving behavior is conducive to the improvement of drivers’ self-management ability and the customization of drivers.

This paper proposes a method to detect the cyber-attacks by cross checking Signal Phase and Timing (SPaT) information and connected vehicle trajectories data. The proposed V2I cyber-attacks detection algorithm includes the following features: i) effective in detecting the authenticity of broad-casted SPaT information; ii) feasible to identify the occurrence of cyber-attacks when the protection function fails; iii) functional in the environment of partially connected and automated traffic; iv) applicable for isolated signalized intersection. Furthermore, a sensitivity analysis on CV penetration rate is conducted. The best performance is observed when the penetration rate is 100%. Under this penetration rate, the experimental result shows that when the sample data size is greater than 350, the accuracy rate of the proposed method reached 94%.

The tunnel sidewall is important in ensuring safety and efficiency. This study investigated the influence of the complexity of the tunnel sidewall pattern elements on driving behavior through driving simulator experiments. Three kinds of sidewall patterns with different complexity (sports icon; sports icon and ribbon; sports icon, ribbon and snowflake) was designed and compared with the tunnel without sidewall pattern, and 27 drivers participated in the experiments. Meanwhile, the speed, the standard deviation (SD) of speed, acceleration, the SD of acceleration, lateral position, accelerator power, and steering wheel angle were used as indicators to explore the significance and effect size of each indicator by one-way repeated-measures ANOVA. Thus, the influence of sidewall patterns with different element complexity on driver behavior is obtained. The results show that a reasonable design of the tunnel sidewall is beneficial to traffic safety in the tunnel and provides a theoretical basis for the design of the tunnel sidewall pattern.

The influence of different types of music on driver visual and driving performance was studied, which was conducted by carrying the experiments on the platform of a driving simulator. A total of four types music including familiar and unfamiliar music, with or without lyric, were involved. The visual parameters of the driver were collected by Tobii glasses. The results indicated that: (1) The cognitive load of the drivers varied under different music backgrounds. The influence on cognition load from music with lyric is larger than that without lyric. The maximal load is under familiar music with lyric. (2) Due to the physiology arousal and cognitive load caused by background music, there is no significant difference of fixation duration, driving speed variance and the reaction time to the emergency among the background with different types of music, and that without music.

The frequent occurrence of crashes on freeway bridge sections has caused widespread concern. This paper takes the freeway bridge section as research unit and uses the homogeneity method to divide the section units of the E’dong Yangtze River Highway Bridge. Monthly traffic crash, traffic flow, and road geometric data in 2018 were used. A zero-inflated negative binomial (ZINB) regression model was developed to explore traffic safety influencing factors for freeway bridges. AIC criterion and BIC criterion were selected for the goodness of fit test. Relative error (Re), cumulative residual (CSR) and mean standard absolute residual (MSAD) were used for the accuracy test. The results showed that the ZINB model can accurately predict the number of freeway bridge crashes. This paper provides theoretical support and reference for improving the safety level of the freeway bridge operation.

We examined the starting characteristic of the first left-turn vehicle, focusing on the disadvantage of parking twice in the existing setting mode. We established the optimization model of signal timing in the left-turn waiting zone to optimize the existing signal timing scheme, reduce or avoid the second stop, and enable the vehicle to pass through the intersection more continuous movement and with less stopping. Traffic simulation software was used for verification and analysis. Comparative before and after signal timing optimization, CO, NO_X, VOC, and other emissions and fuel consumption values, as well as parking times were respectively reduced by 7.37%, 9.84%, 8.86%, 11.29%, and 13.78%. We demonstrate that the optimization model of left-turn waiting zone signal timing can effectively reduce the second stop of vehicles in the waiting zone and reduce the exhaust emission at intersections.

Road traffic safety influences people’s lives and properties. Safety evaluations should not only consider whether an accident happens, but also the potential risks. This paper develops a traffic risk index (TRI) for evaluating the potential risks on urban roads during a specific period. Features are extracted from driving data offered by navigation corporations, then categorized by road class and road type based on the significance analysis. The index is calculated using the TOPSIS method. The results show the different TRI distribution based on urban road classes and types. We also present a time-varying risk map and the correlation with accident frequency. This study can help to assess and predict urban road traffic risks, as well as providing references for personal drivers and relevant policy-making of the traffic management department to prevent road traffic accidents.

We analyzed the applicability of SSAM (Surrogate Safety Assessment Model) model with TTC in China to study the conflict characteristics of interchange diversion connection and discussed the advantages of DRAC. We calibrated the DRAC for Chinese drivers representing the conflict severity based on normal deceleration observation and emergency braking test of vehicles on freeway general sections, interchange exits and entrances, and toll booths. The paper simulates the length of different deceleration lanes and the number of lanes in connection section, obtains the traffic conflict characteristics corresponding to different connection of interchange, and guides the geometric design of the interchange connection.

We systematically studied the relevant factors influencing traffic safety in the exit area of interchange by investigating and collecting relevant road traffic conditions and traffic accident data in the exit area of the existing expressway interchanges. The control variables with significant interaction effects were eliminated and merged using multivariate variance analysis of the main influencing factors. We established a multivariable linear regression traffic accident prediction model for interchange exit area. The accident prediction model was validated by the traffic accident level of 14 interchange exit areas within the scope of the Hang Xinjing Expressway (G60N). The results show that the relative error between the predicted and actual values of traffic accidents in the interchange exit area is small. The traffic accident prediction model is accurate and practical, which can provide a theoretical reference and technical support for the prediction, evaluation and analysis of the accident variation in the exit area of interchanges.

We proposed a traffic operation risks assessment method of large-scale activities based on fuzzy Bayesian network for the precise prevention of traffic operation risks and the rationalization of emergency plans during large-scale activities. Based on the establishment of a large-scale activities traffic operation risks indicator system, we constructed a fuzzy Bayesian network topology map structure based on the Interpretive Structural Modeling (ISM) and quantified the number, state and conditional probability of fuzzy Bayesian network nodes. Finally, we examined the opening ceremony of a major winter sports event at the National Stadium as a case study. We analyzed the traffic operation risks were analyzed using Netica’s reasoning analysis and control variable method function and studied the changes of large-scale activities traffic operation risks under different adverse conditions.

Traffic safety issues have increasingly aroused widespread concern in society, especially traffic accidents caused by severe weather. Due to the existence of the zero cross slope section, the outside lane of the superelevation transition zone of the expressway easily becomes an area of water accumulation and increased accidents on rainy days. Therefore, it is of great significance to study the water accumulation problem of the superelevation transition zone of the expressway on rainy days to enhance the driving safety. Based on the calculation model of water film thickness in HintCAD software, the relationships between water accumulation and highway longitudinal slope, superelevation gradient rate and pavement width were explored, and the corresponding regression models were established. Safety optimization measures for expressway in rainy weather were proposed to reduce the possibility of water accumulation on the road surface at the superelevation transition zone, and to ensure driving safety and operating efficiency.

There are plenty of advantages to steel-concrete composite beam bridges, such as flexibility, high efficiency, economy, and environment stewardship. With the development of the science and technology, steel-concrete composite beam bridges will be further promoted and developed. However, transverse cracks in composite girder bridges usually appear because of the tensile stresses in the concrete deck induced by the negative bending moment at the intermediate supports of continuous composite girder bridges. There are many measures at present to reduce the tensile stress in the negative bending moment zone of steel-concrete composite beams. In order to analyse the improvement of various stress improvement measures, the finite element model is established. The stress improvement measures are studied in this paper including bearing jacking, adding weight and adjusting the erection order of precast bridge decks. Based on our study, reasonable scientific engineering suggestions are given for related fields.

This study focuses on the evacuation scheme for rail transit transfer stations based on the principle of user equilibrium. First, the design of evacuation streamlines is proposed from three aspects: layer itself, layer to layer, and layer to ground exit. On this basis, the evacuation route is networked, and the flow distribution model of evacuation routes is established from the perspective of user optimization, with time as the reference index of the route resistance function. Finally, taking Fuxingmen Station in Beijing as the research subject, an example of an evacuation scheme is designed. The results show that considering the evacuation plan from the perspective of user optimization can better reflect the actual evacuation behavior of pedestrians and find bottleneck areas in the evacuation process. Through optimization and improvement, the casualties and property losses can be reduced, which is very important to the operation and safety of transfer stations.

Due to the existence of zero cross slope section on the superelevation transition zone, the outside lane of the superelevation transition zone of expressways is often prone to water accumulation and accidents on rainy days. It is of great significance to study the impacts of superelevation transition mode on pavement water accumulation under rainfall conditions for enhancing vehicle driving safety. The water film thickness calculation model of road surface is obtained by using simulation model of HintCAD. From the perspective of conducive to pavement drainage, the superelevation transition methods of the expressway were improved, and the drainage effect was verified. It is proved that the superelevation transition method combined using different superelevation transition gradient on transition curve and adding a crown line can discharge the water accumulation of pavement of the superelevation transition zone to the maximum extent.

Spiral tunnels are usually built-in mountainous areas. Due to the high altitude and frequent snowy weather in these areas, the vehicles are prone to slippage and other dangers when they exit the tunnel, which has a greater impact on driving stability. In view of this special road condition, this research analyzes the factors affecting vehicle stability. According to the spiral tunnel design and the characteristics of the vehicle structure, a simulation scene was constructed. Simulation experiments were carried out under normal weather and low-temperature snowy conditions by adopting the simulation driving simulation experiment method, and the changes of stability and driving instability conditions of the vehicle when driving out of the spiral tunnel exit section under different conditions were obtained. The experimental data is regressed and analyzed to obtain the relationship between vehicle driving stability and various influencing factors, which provides a reference for the follow-up related research work.

The length of the upstream transition area in construction areas during highway reconstruction and expansion has an important impact on the operation of road traffic flow and the construction operation. The simulation model of the operation area is established by Vissim simulation software, and the impact of non-motor vehicles is taken into account. The average number of stops, average queue length, and average travel delay are used as evaluation indicators for the optimal length of the upstream transition area. A combination of analytic hierarchy processes and gray correlation method is used for multi-index evaluation. According to the evaluation results, the recommended value of the optimal length of the upstream transition area is obtained.

In order to evaluate impact of traffic flow after the common main stem road accidents and take corresponding measures to reduce traffic congestion, this paper evaluates and analyzes the impact of traffic flow after the common main stem road accidents. The proportion of occupied roads, the duration, traffic saturation and traffic composition are selected to construct an indicator system, which is divided into accident attributes and traffic attributes. Basing on the matter-element model, a post-accident operation impact assessment model is established. The degree of operational impact of different accident characteristics is determined under different original traffic flow conditions. The rationality of the index system, model and parameters calibration is verified by a practical case of the common main stem road in Fujian Province. This paper provides practical references for the management of the common main stem road and the selection of corresponding traffic emergency measures after accidents.

The comparison and selection of high-grade highway reconstruction and expansion engineering schemes is an important part of reconstruction and expansion engineering design. Whether the scheme selection is reasonable or not directly affects the practicability, economy and technicality of the project. This paper puts forward the principles of index selection for the comparison and selection of reconstruction and expansion projects, and establishes the index system for comparison and evaluation in the reconstruction and expansion projects. Using the relevant theory of fuzzy comprehensive evaluation method, the index selection and comparison evaluation of highway reconstruction and expansion plans are carried out, and the optimal plan is quantified. Through the empirical case analysis of the JB expressway in the suburbs of Guangzhou, Guangdong Province, the method system proposed in this paper is verified. The results of this research can provide a reference for the comparison and selection decision of the comparison and selection scheme.

The traffic lane-changing behavior at the convergence section of adjacent tunnel entrance and exit of urban expressway is a kind of micro driving environment with great randomness. The influencing factors include multi-source information like traffic flow conditions, road conditions, driving behavior characteristics and so on. In this paper, the mechanism of the influence of the information sources on the tunnel entrance is analyzed. Seven influencing factors of lane-changing decision, such as expected lane, driver’s temperament, traffic sign recognition, gap between vehicles of back side, fixation probability of interest area, speed difference between vehicles of front side and distance between vehicles of front side are ranked by analysis method of grey correlation entropy. Then, considering coupling of different drivers in the decision-making process of lane change, the driver’s lane-changing decision model is built based on multi-source information fusion.

We propose a modified cell transmission model to simulate the movement of pedestrians’ behavior in the room by considering the following psychology of pedestrians and the behavior of maintaining a safe distance with others. The path selection of pedestrians is driven by potential, which is a comprehensive reflection of the interaction between pedestrians, the distance to the exit and the path capacity. In the simulation, the evacuation space of pedestrians is divided into a series of hexagonal grids, and the sensitivity of the model parameters is analyzed. The simulation results are compared with the actual empirical data, and the flow- density diagram has good consistency. The influence of obstacles at the exit on evacuation is studied and analyzed. It is found that placing obstacles at the appropriate position of the exit can alleviate congestion of the exit to a certain extent and improve the evacuation efficiency.

To reduce the probability and severity of accidents, improve traffic capacity, and ensure efficient operation of traffic flow, the speed limit has become an important means of highway traffic management. Based on the systematic analysis of the existing methods to determine the speed limit value, a posted speed limit value determining model has been proposed in the paper. First, the flat curve, the vertical curve, and the free flow of traffic are taken as reference values for limiting vehicle speed. Second, after comprehensively considering the influence of roadbed cross-section, traffic composition, pavement conditions, large structures, traffic accidents, and operating speed coordination as a correction factor, a multi-factor speed limit value determination model is constructed and verified by engineering examples.

Freeways play an important role in transportation systems but crashes occur frequently on freeways. Crash prediction models for freeway can evaluate road safety status and analyze safety influencing factors. Based on 9987 crashes occurred on Ningbo-Taizhou-Wenzhou Freeway from 2016 to 2019, this paper develops a Random Effect Negative Binomial (RENB) model, which introduces a random effect term into a Negative Binomial (NB) model to explain time correlation of the crash data among different years. In this paper, the NB model and the RENB model are compared in terms of goodness of fit and prediction accuracy. Results show that factors influencing crashes are annual average daily traffic volume, length of research unit, number of lanes and road alignment; the goodness of fit of the NB model and the RENB model is consistent, while the NB model has better prediction accuracy.

To improve traffic safety in highway tunnels, thirteen factors related to environment, drivers, roads and vehicles are extracted as independent variables based on real-world crash reports. The severity (“casualties” or “no casualties”) and configuration (“a multi-vehicle crash” or “a single vehicle crash”) of crashes are taken as dependent variables. To study the correlation of two dependent variables and identify significant factors affecting crash severity and configuration, a bivariate probit model considering the internal correlation of dependent variables is constructed. The results indicate a positive correlation between the configuration and severity of traffic crashes in highway tunnels; the fatality risk of multi-vehicle crashes is higher. Additionally, the higher the proportion of trucks and the higher the grade of highways, the more severe the crashes. Heavier trucks are more likely to cause single vehicle crashes. The results of the study provides some valuable insights for improving traffic safety in highway tunnels.

In order to analyze the significant differences in the duration of expressway traffic accidents under different influencing factors, a method for analyzing the influence factors for duration of expressway traffic incidents based on survival analysis is proposed. Based on the traffic incident data of Shaanxi expressway, Kaplan-Meier combined with Tarone-Ware was used to test the significance degree of different factors on the duration of traffic incidents, and strong correlation factors were extracted. Then the Cox proportional hazards model (test level α = 0.1) was used to study the impact of different factors on the life cycle. The results show that time, daylight, accident causes and vehicle types are the main factors affecting expressway traffic incidents, among which daylight and accident causes have the most significant impact on expressway traffic accidents.

Considering the influence of the state of motor vehicle traffic on the pollutant emission concentration in highway tunnel, the single vehicle pollutant emission factor model of highway tunnels is established. The emission concentration of nitrogen oxides (NOx) and hydrocarbons (HC) is predicted by the proposed model based on the Taihu Lake Tunnel’s traffic data; then verified by the traffic simulation software VISSIM. The results show that the concentration of NOx and HC in the tunnel will increase with traffic volume, the concentration of NOx will increase as speed increases, and the concentration of HC will decrease as speed increases. Finally, the accuracy of the model is verified by applying actual data from the Taihu Lake Tunnel. The model correctly reflects the relationship between traffic conditions and pollutant emissions in highway tunnels and provides decision support for the management and control of highway tunnels.

The temporary Tower Anchorage technology applied to the construction of self-anchored suspension bridges adds safety and efficiency and avoids the prominent problem of using traditional means to affect the existing traffic. The key temporary structure of this construction technology system, important to construction safety, is the tower-beam thrust device, which transfers the horizontal component of the main cables during the hoisting process of the main beams. This study proposes a safe and reliable design of the tower-beam thrust device and defines the applicable calculation method of PBL shear connectors. Taking a self-anchored suspension bridge as an engineering case, the tower-beam thrust device is designed according to the process flow, and the refined finite element model is established to analyze its mechanical properties. The results show that the mechanical properties of each component of the tower-beam thrust device meet the design requirements, which verifies the rationality and feasibility of its design.

To explore the relationship between traffic safety and efficiency and its influencing factors under specific types of weaving areas, this paper selects length of weaving area, ramp traffic volume, mainline traffic volume and interweaving flow ratio as influencing factors; conflict numbers and conflict rate as safety indicators; and delay and average speed as traffic efficiency indicators. Through the VISSIM simulation test, the test parameters were calibrated, and 38 orthogonal tests were designed. Range and variance analyses were conducted and the importance of each influencing factor on each index was obtained. The study found that for traffic safety and efficiency in the weaving area, the length of the interweaving and the ramp flow are the two most important factors; for traffic safety, the mainline traffic volume and the interweaving traffic ratio are of equal importance; for the traffic efficiency, the mainline traffic volume is more important than the interweaving traffic ratio.

Many traffic participants are not aware of traffic misconduct, which leads to frequent traffic accidents, which in turn worsens the traffic environment and increase economic losses. This paper designs a questionnaire survey on the anomie behavior of three types of traffic participants and analyzes influencing factors, such as age and gender, using SPSS21.0 analysis software. A fault tree model is established to determine the key factors affecting the anomie behavior of traffic participants and analyzes the degree to which the key factors produce anomie behavior. FreeFta software is used to analyze and calculate the minimum cut set and structural importance, as well as perform quantitative analysis and calculation of the probability and critical importance. Finally, according to the analysis results, the key factors are summarized, and targeted measures are given to the three types of traffic participants, which is of great significance to alleviate traffic accidents.

Many cities around China have developed shared bicycle programs in recent years. Shared bicycle programs not only provide more user flexibility but also decrease environmental pollution. However, they have faced many problems recently. The purpose of this research is to seek the influencing factors of willingness to use the shared bicycle in Nanjing City, the capital of Jiangsu Province, China. Data comes from a random sample survey selected 20 bus stations in Nanjing City from July to August 2019. Data were analyzed using statistics, correlation, and Principal Component Analysis (PCA) methods. The results showed that the overall willingness to use the shared bicycle in Nanjing is perceived as willingness in Nanjing City. PCA was used and four service dimensions that influenced user willingness were identified: convenience, usefulness in choosing the shared bicycle, security, and ease of use for the shared bicycle.

In the process of railway operation, equipment faults are inevitable; however, establishing emergency disposal plans manually is time-consuming, which may affect the efficiency and safety of railway operation. With lots of on-site historical emergency disposal cases, this paper introduces an assistant decision-making model based on CBR (Case-based Reasoning). A case database based on the case representation is constructed by keywords extracted from text files. The location information is used to perform case screening. Then, the attribute similarities of the detailed information can be obtained through similarity calculation. Meanwhile, the AHP (analytic hierarchy process) is used to obtain the weight vector of each attribute to calculate the case similarities to obtain the most similar case. According to the most similar emergency disposal plan, combined with the train diagram, the emergency disposal plan for the current fault is automatically established, which can reduce the time of establishment and execution.

As the main mode of transportation between cities, the analysis of expressway traffic is of great importance for the movement of people during epidemic prevention and control, economic exchanges between cities, economic characteristics of cities, etc. To efficiently analyze the trajectories of crowd movement on inter-city and round-city expressways, this paper proposes an efficient algorithm for constructing expressway network graphs based on expressway location points. Data cleaning is performed on mobile phone location data to reconstruct the crowd transfer OD matrices on expressways; and location data between origins and destinations and the shortest path algorithm are used to obtain information about the route in the crowd transfer process. A region in China is selected as a case study; crowd transfer information is gathered on the expressway network and the cross-sectional flow of each expressway entrance and exit. The closeness of transportation between its urban area and affiliated counties is analyzed.

Due to the severe shortage of data in the case records of hazardous materials road transport accidents, a de-completion model with missing data based on attribute distribution probability was developed to complete the missing data. Adopting Gradient Boosting Decision Tree (GBDT) as the base learner, the bagging algorithm constructs an integrated GBDT prediction model. The completed data is used to predict accident type, and the analysis of factors influencing the accuracy is based on the predicted outcomes. Compared to other completion methods, the results reveal that the mean square error value of the data after the completion of the attribute distribution probabilistic completeness model is the smallest. Applying the integrated GBDT prediction model, the accident type prediction of accuracy rate can reach 87.3%. Seven accident characteristics have a notable influence on predicting accident type accurately.

The concrete-filled steel tube (CFST) tied arch bridge combines deck system and arch rib into a whole, to bear the load through suspender. Different suspender layout forms have great influence on the mechanical properties of tied arches. Therefore, a 96m span concrete-filled steel tubular tied arch is taken as the engineering background, and three different suspender layout forms (vertical suspender, Nielsen suspender and mesh suspender) are created by using finite element software. Then the internal force law and influencing factors of static and dynamic aspects of bridge structures are studied. The results show that both Nielsen suspender and reticular suspender can improve the vertical stiffness and natural frequency of the structure and reduce the vertical deflection of the structure and the live load bending moment of the arch rib and tie Girder. The results of this paper can provide reference for the design of CFST Tied Arch Bridges.

Prediction and early warning of fog events on freeways is essential for proactive traffic safety management under fog conditions. Understanding the characteristics and mechanism of fog on freeways is the basis for accurate and timely prediction of low visibility. This study aims to analyze the temporal distribution characteristics of fog events and explore the correlation between visibility and meteorological variables. Guanyinyan service area on the G50S freeway in Chongqing, China is chosen as the study case. The results indicate that: a) early morning (4:00 to 9:00) is the most frequent period of fog events, and most fog events last between 10 and 20 minutes, b) visibility level has a strong correlation with wind speed, humidity, air temperature and rainfall.

Automobile intelligent driving systems and communication services bring the widespread application of information technology and network technology. Meanwhile, there are increasing number of exploitable security vulnerabilities and risks in modern vehicles. These risks may cause serious consequences such as casualties and property losses for drivers. Risk assessment is an important activity in the life cycle of automobile cybersecurity and can provide a guarantee for the formation of correct cybersecurity requirements of products. Considering the current wide recognition standard for threat analysis and risk assessment, this paper proposes a systematic risk assessment method for automotive cybersecurity combined with risk assessment use cases. Finally, a typical use case of security-related component IVI is applied to illustrate the proposed risk assessment method and analysis process of cybersecurity assessment results.

The reasonable distance between adjacent intersections of urban roads improves traffic efficiency, but no reasonable regulation exists in the current standard. To control the intersection spacing of urban roads, the minimum spacing of intersection nodes of urban trunk roads was studied. Firstly, the distance between nodes was defined. Secondly, taking the urban two-way six lane road as the research object, the paper analyzed the driving demand of left turning vehicles on the main road between two adjacent intersections. The calculation model of the minimum distance between intersection nodes was established. Finally, based on the traffic flow theory, driving behavior characteristics, lane changing model and queuing theory, the recommended minimum spacing of intersection nodes was proposed. The results show that the minimum spacing increases with the increase of design speed, and greater design speed leads to faster growth rate.

The screening of dangerous road locations is essential in further road safety improvement interventions. The common definition of a dangerous road location by traffic administration authorities around the world uses raw crash count or deaths, but these two measures are often believed to contain randomness. In this paper, we try to screen dangerous intersections at city level using three approaches and test the consistency of these approaches in terms of site, method, and relative ranking. The results reveal the underlying spatial and urban/rural distribution of intersection hot spots in Hong Kong. Moreover, the Empirical Bayes method seems to be the most reliable and consistent method in terms of site and method. Finally, some problems of the current approaches and possible future directions are discussed.

In order to reduce child occupant traffic injuries, more than 40 countries have issued standards and regulations to protect children. This paper systematically sorts out the relevant laws and standards related to child occupant traffic safety in China, and analyzes the influential factors of children road traffic injury by using the data of national road traffic accident information system. In the past 10 years, child deaths accounted for more than 30% of road traffic accidents, and the number of child deaths in passenger car was the highest, nearly 70% of child deaths were among children aged 1-6 years. The proportion of child death accidents was the highest from July to August, on Saturday and Sunday and at 16 to 18 o’clock. The findings of this study reveal the causes of children road traffic injuries, thus contributing to the smooth progress of the study on children road traffic safety.

Based on task analysis and multi-resource theory, this paper analyzes, classifies and decomposes the tasks of urban rail transit dispatchers, and establishes a workload model of urban rail transit dispatchers based on time occupancy. In addition, considering the individual difference of resource demand and the resource conflict between channels, the supply parameter of psychological resource and interference load are introduced to modify the model. Then, the model is validated by subjective measurement and physiological measurement. The results show that the workload model established in this paper is in good agreement with the results of subjective evaluation and physiological measurement, and the model is effective. This helps to reduce operational risk and human error. At the same time, it is of great practical significance to improve the work reliability of subway dispatchers.

In recent years, the functions of intelligent connected vehicles are becoming more and more powerful, which increases the number of electronic control units (ECUs) and the number of external connections, etc. However, these technologies made the intelligent connected vehicles more vulnerable to attack. Intelligent connected vehicles have been attacked frequently, causing serious threats to life and property. Therefore, intelligent connected vehicles need to carry out information security protection. In this paper, cost, delay and information security factors are considered, and according to the characteristics of the attack path of intelligent connected vehicles, an information security protection strategy for intelligent connected vehicles is proposed. The protection strategy improves the efficiency of information security measures allocation and reduces the cost.

In China, there are many freeways with rapid traffic volume growth, congestion and accidents always happen on these freeways. To solve these problems, the original freeways need reconstruction and extension. During the reconstruction and extension process, the freeway must maintain normal access. Freeway reconstruction and extension will bring pressure to the safety situation of the access part. Traffic accidents will increase and the emergency management is very important. The study focuses on the difference of the traffic accidents characters before and after the reconstruction and extension. To decrease the impact of accidents, the emergency management is studied. The key of the emergency management is layout optimization of traffic emergency stations. The layout optimization of traffic emergency stations is also given according to the theory of system optimization, considering the special freeway reconstruction and extension features. The real He’an freeway with reconstruction and extension in Anhui Province in China are applied to test the study.

The diffusion efficiency of rejuvenator in aged asphalt is an important indicator to evaluate the quality of rejuvenator. In this study, hexadecanamide was selected as a representative molecular structure of bio-rejuvenators. Based on the weight ratio of each component and the number of aged groups, three asphalt binder models were constructed for different aging states. The RDF function was used as an important approach to analyze the states of molecular agglomerations in virgin and aged asphalt binder. To calculate the diffusion coefficient of asphalt reasonably, the asphalt-aggregate diffusion model was constructed in diffusion planes from different directions. The influence of temperature on this process was also considered. The average diffusion coefficient of aged asphalt at the aggregate model surface was lower than that of virgin asphalt. However, under the influence of hexadecanamide, the average diffusion coefficient of aged asphalt showed an increasing trend.

Vehicle lane changing behavior is an important factor affecting the road capacity. This paper explores the influence of the disturbance wave generated by lane changing behavior on travel speed and headway of the fleet and proposes a calculation model of the road capacity reduction coefficient. As the frequency of lane changes influences road capacity, this paper analyzes the range of disturbance propagation, the average speed of the fleet and the average headway of two-lanes in the same direction. It further examines the multiple “no influence” and “influential” lane-changing cases and examines the relationship between lane changing actions. Using VISSIM simulation platform, experimental data is obtained. The results show that there is only 4.6% error between the numerical value calculated by the model and the simulation result measured by the VISSIM, which verifies the proposed method.

In order to reduce pedestrian delay and improve utilization of road resources, this study examined the conditions of pedestrian crossing settings at signalized intersections, according to the actual composition of road signal intersection, pedestrians, and motor vehicle traffic. Then, the pedestrian twice crossing facilities of signal intersection set conditions was studied. The signal phase and pedestrian crossing phase settings were optimized, and VISIM was used to verify the results. The results show that when the pedestrian flow is in the 2000per/h -2500per/h and motor vehicle traffic is in the 2500pcu/h – 3000pcu/h range, the reasonable setting of pedestrian twice crossing facilities can improve the efficiency and security of pedestrians crossing the street.

Recognizing transportation mode from raw trajectory is a crucial task in transportation, which benefits travel pattern understanding and traffic management. However, difficulties such as small, unbalanced datasets and extracting efficient features from raw trajectory data persist. This paper proposes a novel image-based feature to represent trajectory, since we find that various transportation modes are easily distinguished in image representation, preserving original trajectory information. The feature’s low rank nature also improves overfitting problems. Additionally, due to heavily unbalanced datasets, traditional classifiers, such as support vector machines, are difficult to perform. To mitigate this, we introduce a Bayes neural network, learning a distribution for each parameter instead of a single value. Results show that our novel image-based feature improves the overfitting problem, and the combination of the image-based feature and Bayes neural network achieves competitive performance on all modes, especially obtaining performance improvements on subways and trains by 13.4% and 42.8%, respectively.

With national economic improvements, more large-scale activities are held in major cities, causing pressure on surrounding road operation. Therefore, this paper studies the average speed of surrounding roads during short-term large-scale activities and analyzes temporal and spatial characteristics of their impact on surrounding roads using floating car and coil detector data. Analysis shows that average speed of the affected road around the venue declines and traffic volume of the section increases significantly two hours before the activity, and average speed of the road decreases and traffic volume increases within 1 hour after the activity. The prediction results show that the average 15-minute speed prediction accuracy of East Second Ring Road, New East Road, North Workers Stadium Road, and East Workers Stadium Road are 94.09%, 88.90%, 78.11% and 84.64% respectively. The prediction results can provide corresponding support for the study and judgment of surrounding road network operation situation during large-scale activities.

Methylene diphenyl diisocyanate (MDI), a common chemical raw material, can greatly improve the high-temperature performance of asphalt. The viscosity of MDI-modified asphalt needs further study. MDI-modified asphalt models were constructed and confined through a molecular dynamic simulation to investigate the effect of the macromolecules of the nucleophilic addition reaction on the modification. It was found that the viscosity of matrix asphalt changes little with the increase of temperature. MDI-modified asphalt with double pyrrole behaves similarly except that the shear viscosity of the latter was larger with increasing temperature. However, the two models containing asphaltene-phenol both showed higher viscosity-temperature sensitivity at temperatures of 368K-383K. Double phenol or phenol-pyrrole may play a role in increasing viscosity in MDI-modified asphalt.

An increasing number of security vulnerabilities have been found in the electronic control units (ECUs) and external communication interfaces of smart cars. Intrusion detection systems (IDS) have become the focus research as an initiative protection measure, yet the key issues are not well resolved, such as detection accuracy and computation overhead. According to vulnerability analysis and the different features of abnormal data under different attacks, an in-vehicle network experimental attack platform and an abnormal behavior characteristic library are built. Then, a low complexity learning algorithm is used to detect simple abnormal features. Next, a more complex detection method is proved to achieve accurate detection of abnormal data for abnormal behavior such as false data injecting and semantic tampering. Our research work makes it easy to understand intrusion detection systems for in-vehicle networks and benefits the implementation of security in the automotive industry.

There are many studies on the design of the parking area of highway service facilities, but there is no clear standard specification. From the existing research on highway service facilities, we find that the scale of a highway observation deck is mainly controlled by the scale of the parking area. In view of the deficiencies of the research, this paper builds a prediction model of the vehicle entry rate of an ordinary highway viewing platform based on a BP neural network, and uses Matlab software and field survey data to test the model. The reliability of the prediction model is verified. According to the parameters such as passenger car entry rate, peak rate, parking turnover rate and vehicle size, the scale of the observation deck parking area under different passenger vehicle traffic volumes on ordinary highways is obtained through the calculation formulas given by existing specifications and research.

This study aims to address cyberattack issues on the connected automated vehicle (CAV) by regarding a CAV platoon as a spring-mass system from the perspective of vibration mechanics. Given the similarities between cyberattacks on the CAV platoon system and an external force on spring-mass systems, we proposed a Fourier analysis approach to formulate cyberattacks into longitudinal vehicle dynamical models based on the fundamental physical law of the springs-mass systems. The proposed approach is applied to various models to generate insights into cyberattack effects. The study applies the intelligent driver model to identify the characteristics of traffic flow dynamics under cyberattack. Simulation results demonstrate that attacking amplitude, frequency, and duration can cause stop-and-go waves and traffic accidents, and show the oscillation mechanism of traffic under different attacking patterns. The findings shed light on innovative solutions against cyberattack and contribute to reducing congestion and eliminating traffic safety concerns.

To explore the relationship between driving skills, driving behaviors, and driving performance, a random investigation was conducted using the Driving Skill Inventory (DSI) and Driver Behavior Questionnaire (DBQ), and 100 valid samples were obtained. Through a paired non-parametric test and Kruskal-Wallis test, it was found that there was a significant difference in perceptual motor skills among different driving experiences. The structural equation model of driving performance was constructed to explore the intrinsic causal relationship between the variables that affect driving performance. By modifying the model, a structural equation model of driving performance was finally obtained. The analysis results show that safety skills have a direct effect on positive driving behaviors, emotional control skills and violations have significant direct effects, perceptual motor skills have a significant direct effect on errors, violations and errors have a significant direct effect on driving performance, and emotional control skills indirectly affect driving performance by directly influencing violations.

This paper aims to investigate the durability of a newly developed powder geopolymer grouting material (S-F powder Geopolymer), which can be used to fill voids beneath a cement pavement slab and repair cracks in cement-stabilized base of asphalt pavement. Water resistance, water absorption, erosion of acid, alkali and sulfate, salt-petering, toughness and anti-freeze-thaw tests were conducted to assess the durability of S-F powder Geopolymer. Results show that S-F powder Geopolymer has good water resistance. The specimen’s strength decreases after soaking in three solutions (HCl, NaOH and Na₂SO₄ solution). The salt-petering characteristic of S-F powder Geopolymer is obtained. In addition, S-F powder Geopolymer has better toughness than PO42.5 and better impact resistance, especially in the early stages. However, the anti-freeze-thaw performance is weak, which is not good for use in the freeze-thaw region. The results of this research can provide references for the field engineering application of this material.

An intersection is a typical collision hot spot in the urban traffic network. At an intersection with many pedestrians, the conflicts between pedestrians and traffic mixing flow and turning vehicles are more serious. The design and consideration of pedestrian crossing time are critical in counteracting the negative influence of conflict between vehicles and pedestrians. This study is aimed at an intersection with relatively large pedestrian volume and traffic flow in a core area of Xi’an and conducts a multidimensional analysis of pedestrian crossing time. Based on the combination of essential data investigation and simulation results, the impact of different pedestrian timing designs on intersection delay and fuel consumption is analyzed. The results illustrate that the variation trend of delay is very different under the diverse pedestrian crossing design. The late start and early end method would be more likely to reduce delays and fuel consumption at the proposed intersection.

The road traffic environment factors that affect the speed limit value of the expressway mainly include visibility and friction coefficient, plane alignment, vertical section alignment, lane number, lane position, traffic volume and traffic composition. These factors are random and uncontrollable, so a multi-factor expressway speed limit model based on fuzzy control is constructed. This paper calculates the correction coefficient of each factor to the reference speed limit value by establishing the stopping line-of-sight model and the method of regression analysis. The speed limit value of fuzzy logic control model was established. Twenty typical sections were selected using fuzzy algorithm to obtain the influence factors of total correction coefficient in the MATLAB environment, respectively to calculate the fuzzy control model and the traditional model of speed limit value, and comparative analysis of the two algorithms to verify the feasibility and rationality of the proposed model in this paper.

Based on vehicle kinematics and physical theory, this paper analyzes the safety and feasibility of the whole process of highway vehicle lane changing behavior, discusses the influence of different factors on lane changing behavior, and obtains the mechanism of various factors affecting lane changing safety. The detailed information of traffic accidents is collected, and the influencing factors are obtained by linear regression analysis. By quantifying the uncertain factors, the safety distance model is established and the simulation environment is established. Finally, the safe lane changing distance of the vehicle under the influence of various factors is obtained. This paper comprehensively analyzes the influence mechanism of a road environment and driver’s own condition on lane changing behavior, which provides a theoretical basis for the development of an intelligent driving assistance system, and can significantly help drivers change lanes safely and improve the safety level of expressway driving.

For hinterland regions with developed water channels and networks, inland shipping plays a significant role in the comprehensive transportation of waterway containers. This paper extracted vessel speed from a VITS dataset with the data capacity reaching 79.7G to analyze the temporal speed distribution pattern. The Entropy-TOPSIS method was utilized in this article for more specific and intuitive reflection of the speed index of different periods in a day. Through integrating Entropy and TOPSIS algorithms, speed indexes were quantitatively calculated. The results revealed that vessels tended to maintain a relatively higher speed at 7:00-18:00 interval, which is consistent with working hours for southern areas in China. The method proposed to consider speed distribution in temporal dimension is novel in this article, and it can be used for future research to measure if the temporal traffic density is stable enough to provide theoretical support for planning, deciding and managing waterway transportation.

Worldwide, road safety of pedestrians has been recognized as a public health concern. Previous studies have been conducted to analyze pedestrian safety at the macro level, but only a few were conducted in China. This study aims at investigating the impacts of potential contributing factors in pedestrian-involved collisions at zonal level in an urban area in China. The study included 138 traffic analysis zones. Both generalized linear regression and full Bayesian macro-level collision prediction models were developed to examine the correlations between pedestrian crashes and multiple variables. Results show that pedestrian crashes are positively correlated with exposure, population density, density of 4-way signalized intersections, and percentage of arterials, and they are negatively correlated with density of 3-way intersections and percentage of collectors. The findings of this study could help decision makers, planners and safety engineers gain better understanding of pedestrian crashes and develop countermeasures to enhance pedestrian safety.

In this paper, the latent variable of “travel ability” is introduced to the study of the travel behavior of the urban elderly for shopping activities. According to the principle of Lewin’s behavior model, the structural equation model is used to construct a correlation model of the shopping activity and travel behavior of the elderly. Based on 53 qualitative interview materials of urban elderly people over 60 years old in Kunming, China, the model is fitted according to the data obtained from 523 valid questionnaires collected at fresh food markets and 512 valid questionnaires collected at supermarkets. It is found that the travel behavior mechanisms of the two types of shopping activities of the urban elderly are different. However, the consistent conclusion is that the latent variable of the “travel ability” of the urban elderly has a more significant impact on shopping travel behavior than does the “age” variable.

Under the background of the integration of transportation and tourism, the demand for reconstruction and expansion of traditional service areas and diversified service areas of transportation and tourism is increasing. The layout of service area is a key link in the construction of service area. Improper setting will cause waste of resources and bring great losses. Based on the analysis of the main influencing factors and layout forms of typical service areas in China, the evaluation index system of service area layout is constructed, and the comprehensive evaluation of service area layout is carried out based on improved entropy method TOPSIS. Results show that the layout of “traffic and tourism” new service areas is relatively better. It provides reference for the reconstruction and expansion of traditional service area and the design of diversified service area of transportation and tourism.

As a new public transport operation mode, the flexible transit system integrates the high efficiency and low cost of conventional public transportation with the flexibility of demand-responsive public transportation. At present, it has been successfully employed in many foreign cities. The operation results show that flexible public transportation is an effective way to solve the problem of public transport service in low travel demand density zones. This paper first analyzes five factors that affect the planning of flexible public transportation stops, and constructs a node importance model based on these five factors, using K-means clustering algorithm to determine the nodes suitable for planning bus stops. The results show that the method is feasible and practical, and can provide a theoretical basis for domestic enterprises to plan flexible public transportation.

Travel-way reorganization has been recognized as a way of negotiating limited road space and ever-increasing traffic demand. This paper evaluates different travel lane inflow restrictions under two travel lane reallocation schemes: (i) standard travel lane width, and (ii) narrow travel lane width. A computation experiment was conducted on a traffic corridor in Xi’an that carries around 6000, 8000, and 10000 person-trips per hour during off-peak, adjacent-to-peak, and AM/PM peak periods. When the inflow restriction for private cars entering the bus-only lane varies from 0.1 to 0.8, lower total travel time of all travelers could be achieved. As the traffic demand further increases, significant influence of lane inflow restriction on total travel time would be found. However, those differences in total travel time among various restrictions could be further reduced after adopting a narrow travel-way design. Meanwhile, the efficiency of traffic systems could also be enhanced for all demand levels.

Intercity carpooling is gradually emerging, helping alleviate intercity traffic during holidays. Promoting intercity carpooling during holidays is rarely seen in the current research. In this paper, the data of RP/SP survey is used to analyze the travel behavior of intercity carpooling. The multinomial logit model is established to research the key factors affecting passengers’ choice behavior of carpooling. Results show that intercity carpooling passengers have obvious characteristics in travel distance, number of companions and process of finding carpooling vehicles. Gender and monthly income significantly influence the choice of carpool A, which travels directly to a destination city, but not on the choice of carpool B, which stops at a city along the way to pick up other passengers. Travel distance, stop time of carpool B, fee discount of carpool B and the departure date delay of railway significantly influence the choice of two carpooling modes.

RongDong District is the pilot area in Xiongan New Area in China, and the public transport demand in this area needs to be further studied. Based on existing planning results, this paper uses the method of city-level comparison for further study. This paper selects three world-renowned transit-oriented cities, Hangkang, Manhattan, and Paris, to analyze respectively the differences in the network shape, scale and travel structure. Then it simulates the public transport modes in these four cities in transCAD by setting boundary conditions, controlling variables, etc., and uses the four-stage method commonly used in transport planning to make a simulation at regular transport scales and under limit road net bearing capacity. Finally, it analyzes and compares the simulation results with two indicators of the proportion of congested roads in the total mileage and the density of congested roads.

Urban characteristics are the basic factors that affect the development strategy and project layout of comprehensive urban transportation. This paper introduces the strategic planning path of a comprehensive urban transportation system based on urban characteristics. It analyzes urban characteristics of Wuhan, including natural geographical characteristics, social characteristics and economic characteristics, putting forth an urban strategic mission of Wuhan, strategic planning position of comprehensive transportation system of Wuhan, and planning principles, methods and guarantee measures to realize comprehensive transportation strategic positioning of Wuhan. Finally, the concrete project layout planning and corresponding guarantee system of comprehensive transportation system construction in Wuhan are discussed. This paper presents a strategic planning path of comprehensive transportation based on urban characteristics from refined urban characteristics, task and situation analysis, strategic aim and position, planning and guarantee strategy to planning scheme.

A multi-level passenger satisfaction evaluation index system is established in this paper based on a customer perception model and hierarchical theory of needs, which includes five first-level indicators of materiality, reliability, responsiveness, assurance and safety and 27 second-level evaluation indicators. Taking the South Square of Jiaozuo Railway Station as an example, based on the results of questionnaire survey, the weight value and membership matrix of each evaluation index are obtained by using principal component analysis and sum product method. The passenger satisfaction score the station is 3.51 by using fuzzy comprehensive evaluation method, ranging from average to satisfaction. The key indicators affecting passenger satisfaction are analyzed by Quadrant graph method and improvement measures suggested for the station operations. The research shows satisfaction research from the perspective of traffic operation managers and passengers can effectively improve the accuracy of evaluation, and provide decision-making reference for relevant departments to improve service level.

Based on the elaboration of the core features of the comprehensive transportation system and the definition of the regional comprehensive transportation system competitiveness, this paper constructs an evaluation index system for the regional comprehensive transportation system competitiveness. This includes 3 dimensions (development basis, development performance and development potential), 9 areas and 34 specific indicators. Finally, this paper measured and evaluated the comprehensive transportation systems competitiveness of 6 coastal provinces in eastern China in 2018.

The service quality of urban public transport is directly related to the travel conditions of citizens and the overall development level of urban transport. To study the influence factors of bus passenger satisfaction, the results of the survey on the satisfaction of regular bus passengers in Suzhou Kunshan were analyzed and studied. Then the reliability and validity of data were analyzed by SPSS. Next, a structural equation model was established to analyze the path coefficient between the latent variables of environment, safety, operation, convenience, price, passenger satisfaction and willingness, and to explore the main factors affecting passenger satisfaction. The results show that improving the bus operation service and environmental comfort can greatly improve the passenger satisfaction, thus effectively improving the willingness of passengers to take the bus and attracting passengers to use the bus, which is of great significance for promoting green travel and alleviating traffic congestion.

Taking the new first tier cities as an example, this paper uses the DEA Malmquist index to evaluate the traffic infrastructure efficiency of new first-tier cities from 2014 to 2018 from static and dynamic aspects. It analyzes its influencing factors by using the multiple linear regression model. The results show that: (1) from 2014 to 2018, the comprehensive efficiency index and pure efficiency index of new first-tier cities are generally low. The scale efficiency index is in the middle level, and there is pronounced heterogeneity in the development of transportation infrastructure. (2) Using a multiple linear regression model to analyze the panel data, the industrial structure, government behavior, and government investment in education negatively impact the efficiency of transportation infrastructure. In contrast, the dependence on foreign investment has a positive effect. New first tier cities combine their own shortcomings to improve their competitiveness.

Air and high-speed rail intermodal service (AHIS) is an emerging way of travel service. In this research, we aim to compare the Random Forest algorithm (RF) with other four classification algorithms including the Logistic Regression algorithm (LR), Gaussian Naive Bayes algorithm (GNB), K-Nearest Neighbor algorithm (KNN), Decision Tree algorithm (DT), in predicting travelers’ ticket buying preferences. The research was conducted at Shijiazhuang Zheng Ding International Airport in 2019 to complete a passenger behavior survey. By comparing and analyzing the classification indexes such as accuracy rate and Receiver Operating Characteristic (ROC) Curve, we found that the RF algorithm has the optimal classification prediction performance. Through the RF algorithm, we predict the travelers’ ticket buying preferences of users and make several suggestions for the AHIS operator by changing different personal attributes and travel attributes.

Walking is an important connection in Transportation Oriented Development (TOD). An evaluation of Walking-Friendliness of a Subway Station is constructed from safety, comfort and convenience. Based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP), the specific meaning, calculation method and weight of each index are given. Then, the standards are put forward for classification of Walking-Friendliness and construct of the AHP-TOPSIS mode. A quartile graph is constructed combining the importance and evaluation score of nine indicators. 66 roads within an 800-meter-area of Dawanglu Station are analyzed, and the results show that AHP-TOPSIS method can meet the actual needs of the Walking-Friendliness evaluation of a Subway Station where it overcomes the shortcomings of single-perspective researches from three aspects of nine indicators, and can provide targeted improvement measures to enhance walking-friendliness, and has a certain reference value for guiding the development of TOD.

Many but not all freight activities are affected by the economy. Taking the data of Shandong Province from 2008 to 2018 as an example, the analysis of the relationship between road transport freight volume and GDP in different regions confirms that economic development is not the only factor affecting freight activities. Using the grey relational analysis method to analyze the freight volume data of railways, highways, aviation and water transportation, the total fixed asset investment of the whole society and the industrial structure and the degree of correlation, the relationship between the total freight volume is indirectly obtained. Results show that the main economic factor that affects freight activities is the GDP of the primary industry, and other factors also have a certain impact. Understanding factors affecting freight transport can have a certain guiding effect on the development of freight industry, thereby optimizing the freight structure and improving freight efficiency.

Bicycle traffic is an important part of the urban transport system. However, bicycle traffic environment is not optimistic, and traditional traffic planning is mobility-oriented. More attention should be paid to the planning of bicycle traffic, especially the accessibility aspects. In this study, bicycle accessibility is viewed as the superposition of spatial and psychological accessibility, which are measured by time consumption cost and psychological consumption cost, respectively. We conduct cycling experiments and use SPSS to analyze the relationship between traffic environment, psychological data and riding time data. Furthermore, this paper calculates time cost and psychological consumption cost using experiment data, and adopt a structural equation model to analyze the relationship between spatial accessibility, psychological accessibility and bicycle traffic environment. The results show that bicycle accessibility has a direct relationship with traffic environment. Finally, some suggestions are given to enhance bicycle accessibility.

County economy is the root of the national economic operation, and transportation is an important support to promote the high-quality development of county economy. Regional airports are usually located in small and medium-sized cities such as counties and are an important part of county transportation, and their construction is an important way to solve the problem of low access to county economic infrastructure. Therefore, how to realize the coordinated and adaptive development of regional airports and county economies has become an important topic in the current economic industry. By constructing a comprehensive evaluation index system for the adaptability of regional airports and county economic development, this article adopts the principal component analysis method to study the adaptability of 11 regional airports and their districts and counties in Sichuan Province in 2019. The results of the adaptability level presenting different characteristics in different regions are analyzed.

To better realize the integration of bike-sharing into the urban public transport system and solve the problem of the last kilometer of urban traffic, this paper is based on the 15-day Mobike cycling data in Dongcheng District, Beijing. By using complex network analysis method, this paper analyzes the degree of node and the centricity of medium in the bicycle-sharing network. The results show that the nodes with higher degree and centricity of medium in the bicycle-sharing network are mostly bus stations, although bike-sharing plays an important role in connecting urban public transport.

The purpose of this paper is to establish a reliable model to predict the parking demand of a hotel parking lot in real-time and then analyze the feasibility of opening shared parking spaces. Based on the cluster analysis of hotel parking demand on weekdays, this paper introduces the time series model of econometrics to predict the short-term parking demand. The model calibration and parameter estimation are carried out on the Eviews 8.0 platform, and the appropriate model is selected for the real-time prediction of parking demand. Combined with the measured data, the fitting is proved to be ideal. This paper puts forward the concept of the cumulative frequency of the proportion of vacant berths. According to the prediction results, the judgment conditions of hotel shared parking are given, and the sharing time period is reasonably divided, and good results are obtained.

To explore the influencing factors of residents’ intention to use bike sharing for low-carbon travel, this study added environmental awareness and habit to the theory of planned behavior (TPB). Based on the structural equation modeling (SEM), the questionnaire data of 322 respondents were empirically analyzed. The results indicated that habit, perceived behavioral control, attitude and environmental awareness had a significant and positive impact on the intention of bike sharing for low-carbon travel. Furthermore, habit posed a powerful indirect effect on the intention of bike sharing via perceived behavioral control and attitude. In addition, the empirical results also validated the rationality of the extended TPB model in examining residents’ low-carbon travel intention with bike sharing. This study enhances the current understanding of bike sharing travel behavior, and provides enlightenment for the government to formulate policies and guide residents to change their travel concepts.

With the acceleration of urbanization, inter-jurisdictional travel is becoming more and more common between metropolis and small towns in its fringe areas. In this study, firstly, researches on metropolitan fringe towns are reviewed. On this basis, an index system of trip characteristics is established and the acquisition methods are given by using multi-source data. Secondly, taking Beijing and Dachang County as examples, the feasibility is verified. The result shows: The average daily traffic of Dachang County is 695,000 times, of which 385,000 times cross the region; Rigid demands, like work commutes, is the main purpose of travel; The average inter-jurisdictional travel time is more than 1 hour and result in a bus share rate of only 4%; The traffic is concentrated on several main corridors, thus aggravate the traffic congestion.

To determine the rationality of the site selection for the new public parking garage, this paper applies the traffic impact analysis (TIA) to the site selection decision analysis. First, based on the application programming interface (API) provided by the Gaode Map, the average speed of the road segment is obtained using Python, which relieves the pressure of manual investigation. Then, the point of interest (POI) types are reclassified, and the POI acquired by Python is visualized to guide the division of traffic zones. Finally, the analysis result of the public parking garage in Changchun is obtained using TransCAD. The result shows that the completion of the project will not have a serious negative impact on the surrounding road network, so the site selection is reasonable. The application of TIA enriches the site selection decision methods, and the case study shows the feasibility of the TIA.

Electric buses (EBs) are gradually replacing fuel buses in transportation systems. Previous studies have shown that limited driving range and frequent charging demand because of limited battery capacity could impede network operation. This paper aims to improve the efficiency and economy of EB systems through the joint optimization of EB network planning and operation. We established an integer linear programming model to determine bus lines, location of charging stations and departure frequency. A hybrid heuristic algorithm combining genetic algorithm (GA), simulated annealing (SA) and neighborhood search (NS) is designed to solve the model. The numerical example verified the effectiveness of the model and algorithm. The calculated results could ensure network operation and overcome the shortcomings of EBs. Moreover, the results of sensitivity analysis provided a more appropriate value of construction cost, battery capacity index and power consumption index.

In this study, we analyzed the existing standards for station construction and related research and clarified the requirements of station construction. We proposed the configuration requirement of the transfer facilities based on the analysis of passenger transfer behaviors in integrated passenger transport hubs. A new model is developed to determine the scale of transfer facilities, including passenger transfer squares, transfer halls, transfer channels and other transfer facilities. The proposed method was designed to match the capability and function of transfer facilities according to the development demand of integrated passenger transport hubs. We provide a general formula in this study that is ready to be applied to a generalized integrated passenger transport hub.

In this study, we established an evaluation index system for the service quality of expressway service areas and an evaluation method to scientifically evaluate the service quality of the expressway service area, clarify the problems in the service, and improve the service level. We chose four dimensions of service quality evaluation through literature analysis, expert interviews, and investigations: planning and layout, operation service, management system, and service environment. We also explained specific evaluation indexes for each dimension. Due to the complexity of evaluation factors and the hierarchical nature of evaluation targets, we used a fuzzy and comprehensive evaluation method. One service area was chosen to carry out the evaluation to verify the effectiveness of the proposed evaluation method. The results supported the feasibility of the evaluation index system and evaluation method and provide theoretical support to evaluate the service quality of expressway service areas.

Humans are looking for sustainable strategies to reduce automobile usage. Studies have found built environments such as density, diversity, and design have an impact on commute mode. Transportation facilities are one of the specific dimensions of built environment; the studies about the effect of it on mode choice are limited. Using self-administered survey by smartphone apps in Xi’an, the multinomial logistic regression model is used to analyze the impact of transportation facilities on residents’ commute modes, including walk, car, bus, subway, bike, e-bike, and shuttlebus. Finally, it is found that residents are likely to commute by bike when there are shared bikes near living or working locations. People are likely to commute by car and shuttlebus when bike and vehicle lanes are built on different horizontal levels. The results also indicate that with the growth of vehicle lanes, residents tend to commute by bus, subway, or e-bike.

With the development of big data technology, research on the temporal and spatial characteristics of urban traffic distribution has received more attention. This paper applies different grid meshing methods to mine frequent movement patterns in time and space with taxi trajectory data to discover the spatio-temporal distribution of taxi travel demand. First, we proposed three different meshing models (triangle, square and hexagon) to divide the study area into grids. Then, we used a matching algorithm to index the taxi trajectory points into the grid. Finally, we mined the taxi, temporal, spatial and spatio-temporal frequent movement pattern according to the trajectory. The experimental results show that the method proposed in this paper can effectively extract frequent movement patterns, and different grid models give different pattern extraction results. This study can provide taxi drivers with a path basis for frequent orders and help improve the efficiency of taxi operations.

To analyze the rationality of the planning scheme of the multimodal passenger transportation hub and improve the efficiency of traffic transfer, we established a practical and feasible comprehensive evaluation index system of the multimodal passenger transportation hub from qualitative and quantitative aspects. We created an evaluation method by utilizing the Analytic Hierarchy Process (AHP) to carry out the comprehensive evaluation of the multimodal passenger transportation hub, and applied it in real cases. Research shows that this method can be used to evaluate the existing problems in facilities and equipment, organization and management, transportation services and other aspects of the multimodal passenger transportation hub, and provide a scientific and reasonable reference for the planning and implementation of the multimodal passenger transportation hub.

Bicycles are an important part in non-motorized traffic and play a key role in green transportation and sustainable development. We proposed a model for an urban street bikeability evaluation system based on relevant planning and research. First, we constructed an evaluation of bikeability from three levels of safety, comfort and convenience. The specific meaning and calculation method of each index in the evaluation system are given: AHP (Analytic Hierarchy Process) and EWM (Entropy Weight Method) is used to solve the weight of each index. Then we proposed standards for classification of urban street bikeability. Lastly, we used Beijing as the target area. The urban street bikeability was evaluated, analyzed and visualized by applying ArcGIS. This research offers a direction to optimize street bikeability, and the results can also be applied in route planning to improve non-motorized traffic experiences.

The drop-off platform is a crucial piece of infrastructure of the passenger terminal of the large-scale railway station. This paper focuses on exploring the influence of lane layout design of the drop-off platform on its performance with the aid of microscopic traffic simulation. Based on the field survey of some large-scale passenger terminals, it summarizes six critical elements of geometric design and traffic control of the platform. Orthogonal experiment design is used to decide the attribute combinations of these elements in the simulation experiments. The influences of the elements are investigated by analyzing the simulation results through the MANOVA. The main effects of each element and the interactive effects of multiple elements are identified in the analysis. Finally, we discuss how the conclusion was extracted from the results by referring to geometric design and traffic control of the drop-off platform in practice.

User-assigning is one method to avoid imbalance in bike-sharing systems. However, the current user-assigning method has loopholes that provide opportunities for some users to obtain rewards in an improper way. We classify users into three categories according to the way the rewards are earned. By analyzing the behavior characteristics of users, we propose an algorithm to identify different type of users. The final rewards will depend on whether the bike redistribution is achieved and how it is achieved. Our method can help the system identify the profit-making behavior of the users and improve the legitimacy of the bike redistribution, thus reducing the total operating cost of the system and improving the service level.

We collected data surrounding the in-station number of people and the passenger flow data of the main stations in Henan Province during the Spring Festival using Tencent’s big data platform. Using this data, we created a heat map of the passengers flow time in-station, the daily passenger flow statistics and the radar map of daily passengers flow peak probability. We systematically studied the time distribution pattern of passenger flow during the Spring Festival in the six stations of five cities. The results we obtained are a significant guide for station ticket window setting, security scheduling and inbound and outbound passenger settlement plans.

With the development of collecting methods and information technology, more traffic detector data can be used to study traffic phenomena. However, the direct use of original data with quality problems to the transportation applications would result in unstable and unsafe application results. Therefore, it is necessary to develop an effective model for road traffic flow data cleaning and repair, so as to obtain the correct traffic information and grasp the law of traffic demand. This study, concentrating in Shanghai urban road intersections, uses SCATS and FCD data as sources and establishes a cleaning and repair model to preprocess data from detectors. The precision of the model is validated by corresponding data. By displaying the characteristics of the running speed and flow in urban roads, we find that the error of the model can be controlled at a reasonable level.

The traditional rail transit operation organization mode can no longer meet the development of transportation in infill development stage and the flexible needs of passengers, especially in clustered city in mountainous. It is necessary to adopt a reasonable transportation organization scheme to match transportation capacity and passenger demand. First, this paper introduces the characteristics, corridor patterns, and rail layouts. The application scenarios and advantages and disadvantages of various operation organization modes for such cities are analyzed. Next, based on the data, a corresponding rail transit operation organization mode is proposed. Finally, suggestions are made for the rail transit facilities and equipment required by the flexible operating organization model. Through the research on the flexible operation organization of rail transit, we provide theoretical and technical support for the safe operation of rail transit and comfortable travel of passengers in clustered city.

This paper quantitatively verified the induced effects on traffic of providing free highway access on major holidays by using measured coil data of G40 Yangtze River Tunnel & Bridge in 2018. It was showed that the policy implementation had a significant induced effect on small traffic with Free Holidays records generally distributed within the top 5% range of annual values. The mean of small traffic flow on Free Holidays was approximately 1.82 times of that of annual traffic flow, and was quite close to the boundary of sum of mean and triple standard deviation of annual flow.. Data support and rational analysis for induced traffic can provide reference and guidance to a certain degrees for the supply levels of transportation facilities in terms of scale and rank. Objective assessments and risk evaluations can be provided as well for deeper understanding of traffic effect caused by policy.

The cooperative vehicle infrastructure mode sometimes is called C-V2I, which is recognized as one of the paths to quickly realize automatic driving. Many new infrastructures required by C-V2I have been included in the national construction plan. The large amount of data generated by vehicle-to-road communication which belong to road operators makes it possible to provide more service. Meanwhile there are many new management requirements that will emerge. This article focuses on the main stakeholder-road operators who have needs and thoughts in case of C-V2I. These needs and thoughts are summarized into an issue framework during different stages such as construction and operation periods, different development levels of vehicle-road coordination, and for different management goals such as higher efficiency, higher service level and increased safety.

Car sharing is a new type of travel mode under the sharing economy and is in the initial stage of development in China. Both car sharing and taxis have attributes of public transportation and private transportation, but there are differences in business models, service processes, and pricing. This paper compares the characteristics of car sharing and taxis by: travel time, distance and spatial distribution, based on taxi GPS data and EVCARD, China’s largest car sharing service provider, order data. The results reveal a certain complementarity between car sharing and taxi travel. Car sharing travel has morning and evening peak characteristics, but is less relevant to commuting. Car sharing is mainly used for medium and long distance and suburban related travel. It is suggested that car sharing can be used as a supplement to long-distance and cross-regional public transportation in the suburbs, improving the service level of long-distance suburban travel.

In this paper, we analyze the urban travel structure, explore the travel distance characteristics of various travel modes, and propose a mode split method which conforms China’s urban travel structure that can effectively improve the efficiency and accuracy of the demand analysis of urban transport system. This paper firstly analyzes the travel distance distribution characteristics of residents in all modes and each mode by using the travel survey data in Pingdingshan. Then, the travel time value is calculated. On this basis, the mode split rate model is constructed. Finally, the solution is programmed with Python, and the optimal and current mode split rates of each traffic mode are compared. This paper quantitatively analyzes the travel structure and the overall operating efficiency of the traffic system in small and medium-sized cities, and provides theoretical support for the optimization of traffic mode structure and the formulation of traffic policy.

In the great metropolitan area, under the low carbon transportation framework and imbalanced job-housing situation, some people choose to travel through non-private vehicles. Some governments would endorse such behavior by all means as congestion gets worse. For this study, the 2017 National Household Travel Survey is selected, targeting the individuals in urban areas who travel by walking, bicycling, public transit, and taxis for their work trips. A discrete choice (multinomial logit), unsupervised (k-means clustering and principal component analysis), and supervised (naïve Bayes classifier and random forest) models are fitted to test the predictive performance, with the vision to deeply master the system partial demand and design a better traveling system for citizens indirectly. The results show that unsupervised methods for predicting work trip mode choices needs to be further discussed while some supervised learning methods could be considered as a promising reference for predicting travel modes.

The establishment needs to investigate the supply chain variables and freight trip activity to understand its impact on the freight system. With Tiruchirappalli as a study city, an Establishment-Based Freight Survey (EBFS) was conducted in the pure receiver establishments, resulting in 212 samples. The supply chain variables were categorized into three groups: establishment, commodity, and delivery characteristics, to understand micro-level freight trip activity in the urban area. Dataset was cleaned, and the descriptive statistics analysis was performed. Then the relationship between freight trip activity and supply chain characteristics was statistically investigated. Freight trip generation (FTG) models were developed to explain the urban freight trip activity using ordinary least square (OLS) and soft computing methods. The suitable FTG model, which provides accurate and better results, was identified. Recommendations and practical implications were presented to solve urban freight system problems, transforming into a sustainable city.

In order to evaluate the service quality of urban public transit networks accurately, this paper selects indicators from three aspects—line network distribution, service level and environmental influence—to establish a bus network operation service three-level index evaluation system (TIES). Due to the traditional analytic hierarchy process (AHP) having strong subjectivity in determining weights, this paper uses the entropy method to optimize the determined weight, and proposes the entropy hierarchical fuzzy evaluation (E-FAHP) model more objective and comprehensive. Based on the 9 bus lines in a certain area of Xi’an, selecting indicators and using two models to evaluate. The result shows the E-FAHP is 78.26 and the hierarchical fuzzy evaluation (F-AHP) is 80.17, and comparison of the results find the environmental influence occupied the same proportion as other aspects in E-FAHP. This research can provide a reasonable and scientific optimization for bus services and improve the attractiveness of public transportation.

With the increasing number of recreational vehicles (RVs) and the increasing travel demands, the construction of RV campsites at expressway service areas has become one of the important function expansion of integration of transportation and travel in expressway service areas. This research discusses the idea of RV camps in the expressway service areas according to the demand for RV travels. Based on the rational and effective utilization of resources and the promotion of economic benefit of service areas, this research proposed the classification of RV camps in service areas, and the principles and method of the layout and location selection of the RV camps in the expressway service areas. This study also made an empirical analysis.

In order to improve service quality and get user demand, the group has carried on the expressway service area service demand questionnaire survey from 2014 to 2017 which involving nearly 2000 people and Jiangsu, Shandong, Liaoning, Henan, Shanxi and Inner Mongolia totaling 6 provinces. Based on survey data, the paper analyses the regional differences and characteristics on service quality of the expressway service area, and then puts forward the corresponding suggestions on meeting user diversity requirements, improving the service quality and operation management ability, realizing the sustainable development of the service area.

Highway passenger travel demand is difficult to predict. In order to forecast travel demand accurately, highway passenger kilometers is used to study population, gross domestic product, highway kilometers, high-speed railway operating kilometers and car ownership. The constructed elasticity model is measured using panel data at the city and provincial levels of China for several years. Results show the growth of high-speed railway operating kilometers will decrease highway passenger kilometers. The high-speed railway operating kilometers elasticity of highway passenger kilometers is -0.074, meaning a 1% growth of high-speed railway operating kilometers will decrease 0.074% highway passenger kilometers. Results show that highway passenger travel demand forecasting under the development of high-speed railway should consider trend traffic volume (influence of population and GDP), induced traffic volume (influence of highway kilometers), and transfer traffic volume (influence of high-speed railway operating kilometers). These provide valuable suggestions for the construction of a sustainable passenger transportation system.

Rural passenger transport is an important way for rural residents in China to travel. There are certain problems in the operation and policies of rural passenger transport, such as unreasonable operational mode and lack of policy support. In the vast rural areas, different regions have different levels of development, and their rural passenger transport development levels are not uniform. The paper proposes a method for dividing rural passenger transport development stages, including the determination of classification indicators and the calculation method of each regional score. The study takes the rural passenger transport in Ganzhou, Jiangxi Province, China as an example, conducts detailed investigation and research in the field, and summarizes the main problems existing in rural passenger transport through a large number of current data analysis. Finally, planning methods for each type of development level are proposed.

The Greenway plays an important role in easing the traffic pressure, increasing green space, enhancing the overall image of the city and increasing the tourism influence. As a famous tourist city with many green ecological resources and humanistic foundation, Lhasa, a core city in Tibetan areas, is the best base for greenway construction. The purpose of this paper is to propose the scientific site selection scheme of the first riparian greenway in Lhasa. It considered the specific conditions and characteristics of Tibet and then set up the index system for Lhasa. The improved grey relation analysis method based on analytic hierarchy process (AHP) is used to make up the defect of grey relation analysis where all of indexes are treated equally and makes the selection more scientific. Lastly, the optimal location scheme for the Lhasa Riparian Greenway is obtained, and the best plan is analyzed and proposed.

The network operation of urban rail transit has put forward higher requirements for emergency management. The Bridging Bus has become an effective emergency response strategy under the disruption of urban rail transit due to its advantages of good mobility, flexible deployment. This paper takes the bridging bus service under the disruption of urban rail transit as the research object and proposes a bridge bus scheme design method. This paper analyzes the passengers’ origin-destination (OD) demand from two aspects: 1) the affected form of passengers and the subsequent travel behavior; and 2) the model considers the nonstop routes. An optimization model with the objective of minimizing the total travel time of passengers is constructed and solved by the Elite Genetic Algorithm. We apply the proposed model to Suzhou Metro to verify its effectiveness in comparison with the traditional bus bridging scheme.

Transportation asset management (TAM) plays an important role for transportation infrastructure, which requires deciding the efficient maintenance and rehabilitation (M&R) treatments to a transportation network in order to obtain a satisfied network. However, the decision is not easy as the decision makers may not have adequate knowledge to understand and model their problems. Hence, this paper presents a two-phase optimization approach to assist the decisions on M&R treatments for transportation networks. It helps to clarify the problem at hand in the first phase and provides high-quality solutions as sound decisions in the second phase. As demonstrated in the case study, this approach can effectively handle multiple objectives and constraints, clarify the addressed problems, generate Pareto solutions, and therefore lead to an informed decision of M&R strategies. Hence, this optimization approach can greatly assist the management decisions even when decision makers do not have adequate knowledge about their problems.

There are a lot of Automotive Vehicle Identification (AVI) devices in urban traffic environment in some Chinese cities. Vehicles will leave a series of timestep-location records when travelling through the road network, which is a valuable data source for macroscopic traffic supply-demand analysis. A method using the AVI data based on the Arcgis platform to reconstruct vehicles’ trajectories is proposed, which can obtain nearly full sample of vehicles’ reconstructed trajectories in the road network with devices. The macroscopic features of traffic demand are explored based on the reconstructed trajectory data. Through visual analysis, the distribution of traffic OD demand, traffic volume and spatial-temporal changes are obtained. Additionally, the relationship between traffic supply and demand is evaluated from the macroscopic view.

Time-dependent origin-destination (O-D) is vital for dynamic traffic simulation and advanced traffic information systems. However, acquiring time-dependent O-D is challenging because large-scale and time-varying traffic data are required. This paper proposes a method of time-dependent O-D estimation based on multi-source data fusion. License plate recognition data and traffic flow survey data are integrated; time-dependent O-D is obtained synchronously. First, travel trajectory is classified according to actual data; the shortest path algorithm is used to obtain O-D. Then, combined with obtained O-D and main section flow survey data of the expressway ramp flow, the complete O-D of the whole expressway is obtained through O-D inversion; the time-varying distribution ratio of O-D is obtained by road section flow. Finally, a dynamic traffic simulation model of Suzhou expressway is constructed by DTALite; traffic condition data from AutoNavi are utilized to validate model performance. Results show that accuracy of the time-dependent O-D estimation model is acceptable, which can help to obtain time-dependent O-D for large-scale traffic networks.

With the development of urban construction, increasing demand for transportation in the large passenger flow corridor of the city space, limited channel resources, and shortage of land resources, it is difficult to avoid the situation of expressway and rail transit sharing the same channel. The existing engineering cases in China include one expressway and one rail transit line, with no precedent for the expressway and two rail transit lines to share the same channel. This paper takes Wenzhou S3 line and Wenrui Avenue expressway as the engineering background and considers people-oriented factors, environmental impact, rational construction sequence, land use, adherence to the premise of urban road network layout, traffic, and economically coordinated development. The integrated layout plan should not only meet the traffic function requirements of the expressway itself, but it should also coordinate the overall construction of multiple traffic modes such as city lines, which is reasonable and feasible.

Rail transit is the backbone of urban public transportation, undertaking most of the passenger flow. Attractiveness of rail transit is directly affected by layout and accessibility of rail transit stations. However, classic accessibility evaluation methods do not fully consider the comprehensive impact of station and external space on accessibility. This paper comprehensively considers influence of passenger flow, land use, transportation network, and connection methods on accessibility and establishes a comprehensive evaluation model for accessibility based on indicators including attractiveness, population, road density, and public transit facilities. The paper studies several important stations of Metro Line 1 in Kunming, China and conducts a comprehensive evaluation of the accessibility of typical metro stations. Results show accessibility of line stations gradually decreases from city center to suburbs. Correlation between accessibility and indicators was verified, and conclusions about urban planning and transportation development were drawn, which provided important references for future planning and construction.

This paper introduces the operation and construction of the rail transit system and uses data of Automated Fare Collection (AFC) System to analyze temporal and spatial passenger characteristics from three aspects of urban rail transit stations, lines and network, taking Suzhou Rail Transit Line 1, 2, and 4 and its branch line as the main research objects. Through massive volume of AFC data, the research finds the passenger flow of Suzhou rail transit lines is centripetal and tidal, and the passenger flow of the line section is uneven, especially the passenger flow of Line 1. According to the characteristics of the urban rail transit system, the paper proposes several operational management measures, such as to increase trains in sections with large cross-section passenger flow, pay more attention to stations with large peak hour coefficients and super peak hour coefficients, and use passenger flow diversion measures.

The development of on-demand ridesharing platforms has reshaped commuters’ travel behavior, enabling commuters to decide whether to become ridesharing drivers or passengers in real-time. To examine the impact of commuters’ decisions and ridesharing platforms’ strategies on the equilibrium state in the morning commute problem, this paper formulates an equilibrium framework to characterize commuters’ mode choice and travel time choice behavior based on the bottleneck model. The time-varying mismatch problem and commuters’ heterogeneous preference for ridesharing mode are considered. Analytical sensitivity analysis is conducted to discuss how market share of ridesharing mode changes with the platform’s pricing strategies based on equilibrium state. Optimal strategies for profit and market share maximization are also studied. We find that supply of ridesharing drivers must be coordinated with passenger demand when the optimal state is achieved. Results are further demonstrated to verify the analytical findings. Our analyses provide insights for pricing strategies of ridesharing platforms.

The ride-hailing industry is booming, but its pricing standard still has problems. By analyzing the current situation, this paper establishes corresponding objective functions and constraints through demand side, supply side, and societal benefit maximization using the NSGA-III optimal algorithm for multi-objective function. The pricing model was applied in a case study of Xi’an. The result considers lowest total social cost or passenger’s lowest cost as the goal. Optimal solution for unit price is 3.5 yuan/km mileage and time unit price is 1 yuan/min, and the car-sharing rate is 0.12%. Optimal time unit price and optimal mileage unit price in Xi’an is 0.6 yuan/minute and 1.4 yuan/km when considering the target as maximizing drivers’ revenue. For the multi-objective function, the paper gives a set of optimal solutions based on a Pareto optimal frontier, for which the mileage unit price is 3.5 yuan/km, and the time unit price is 1 yuan/minute.

With the expansion of its service scope, the complexity of the subway network continues to increase. The most obvious feature is the increase in the number of stations. Understanding the similarity of land use around subway stations, the development degree of surrounding areas and the distribution of supporting facilities is of great significance for future planning and construction of stations. POI data is an important basis for analyzing land use around subway stations. This paper proposes a method for quantifying the POI indicators of stations based on the TF-IDF algorithm, and on this basis, performs cluster analysis to find stations with similar land use properties. Finally, taking the Beijing subway network as an example, the classification results of different types of stations are explained.

In Mainland China, only Beijing and Shanghai have multi-airport systems at present. A few other cities are constructing or planning a second airport. In this paper, we analyze the performance of airport choice by passengers in Shanghai, to study the key factors which affect these choices. We find that the most important factors are the destination and frequency of the flight, and the second most important factors are the price of the ticket and the ground accessibility of airport. Although the approval of regular destinations and schedules of the flights are under the administration of Civil Aviation Administration of China to some degree, passengers’ choice of the airport dominates the market, and affects the operation strategy of airports and airlines. In the city multi-airport system, each airport plays a different role in transportation function, given competition and cooperation in the civil aviation markets.

Due to a large number of customers with different service requirements being scattered across locations, express companies are constantly facing the problem of untimely delivery. The uncertainty of customer demands and travel times make the problem more challenging. To address this problem, this paper considers vehicle routing problems with demand and travel time uncertainty for express delivery, from the perspective of robust optimization. We construct an uncertainty set to make sure all the possible realizations of uncertain parameters that have been bounded. The robust optimization model of express vehicle routing yields solutions that minimize the total distribution cost and penalty. A genetic-based hyper-heuristic algorithm is proposed to tackle this model. We perform a series of experiments to validate the proposed model and algorithm. Experimental results clearly show the strength of handling uncertainties and the superiority of the presented robust optimization approach over the original one.

As the key part of metro trains, on-board automatic train control (ATC) systems play the functions of train operation control preventing speeding. Aiming at the problem of lifetime status evaluation in the life cycle management of on-board ATC equipment, an indicator system of influencing factors and an evaluation model were established, and a cloud model-based lifetime status evaluation method for on-board ATC equipment was proposed. Combined with a practical case of a metro line in Shanghai, the applicability of the method was verified, and targeted maintenance suggestions were given. It is expected to provide a reference for the ideas and methods in the life cycle management of rail transit signal system.

GPS data has been applied to traffic and emissions analysis of operating passenger cars (OPC) for many years, however, there is little research on the difference between the occupied state and vacant state. Based on GPS data, this study focused on the emission differences of two states (occupied and vacant state) on different dates (weekdays, weekends, and holidays). Combined with the software MOVES and emidssion rate model, four emission calculation methods were proposed to reveal the emission characteristics of OPC at different levels (mesoscopic- and micro-level). The results show that the emission factors (EFs) of the vacant state were about 0.6 times that of the occupied state, and the emission of vacant state accounted for about 20% of the total emissions. Moreover, EFs and total emission had a significant weekday/non-weekday pattern which was related to peak hours. This study provides basic comparative algorithms for passenger car emissions.

With the development of Chinese society and economy, and as urbanization accelerates, highway mileage will continue to increase, while motor vehicle ownership increases, resulting in an increase in traffic accidents. To further reduce the number of traffic accidents and associated losses, this paper obtains the features of Chinese traffic accidents through a macro analysis of Chinese accident data, and combined with Chinese real traffic accident data to conduct a more in-depth study of the characteristics of traffic accidents in China. At the same time, this paper analyzes the functions, advantages and disadvantages of the ADAS system and the network technology, and obtains the working conditions that can be solved by the network technology.

Walking trips are an important travel mode in people’s daily life, and the evaluation of the friendliness of urban pedestrian network has been focused more with the development of green travel. In this paper, linear streets are utilized as the basic research unit, POI facilities and weight classification are localized to evaluate walking accessibility, an evaluation of walking environment is carried out through the four proposed indicators, the result of walking accessibility and environment are integrated to obtain the final score, and the score is divided into four levels, each of which is described. Finally, an experiment is conducted to evaluate the pedestrian network in the downtown of Nanjing City, and results demonstrate that streets there generally score low on the walking environment. Even in the central area of the downtown, there are still more streets with lower pedestrian environment scores.

Over-speeding is a major cause of traffic accidents. Proper speed limits provide guidance for drivers and help in improving traffic safety. This paper examines driving behaviors under different speed limits and proposes a speed limit optimizing methodology. First, vehicles steering wheel angles, acceleration, and the distance between the specified vehicle’s center of gravity and centerline of the lane were compared using multiple independent samples nonparametric tests to study the influence of different speed limits. Then, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and PCA (Principal Component Analysis) methods were used to determine the optimal speed limit. These methods were applied in the driving simulator and the results prove that these methods are effective. The results also suggest that speed limit has a significant influence on the lane changing and overtaking driving behaviors. Therefore, these methods could be applied in real traffic management to create a safe and efficient driving environment.

As a special road structure, dual-branch on-ramp has more complex operating characteristics than other ramps due to different origins of vehicles. To improve operational efficiency on main lines and guarantee the fairness of ramp vehicles, this paper proposed a coordinated dual-branch on-ramp metering control algorithm which considered the waiting time of each lanes on ramps as optimal condition to assure fairness. In the meantime, the performance of the proposed algorithm was compared with several conventional ramp metering strategies through a simulation experiment. The simulations are conducted based on the data collected from Dongbang interchange of G4221 expressway which is one of the busiest highways in Jiangsu, China. Then, two performance indexes are obtained and analyzed. The results indicate that the coordinated control strategy has optimal improvement on the mainline and the whole system.

Irrational signal timing leads to serious traffic congestion. However, the current research on signal timing has limitations on eliminating the dimensional influence between optimization indexes. The value of the index was rarely obtained via simulation before, and the application of Simulink on signal timing is lacking. This paper presents a signal timing method for an isolated intersection based on the Simulink. First, a queuing system is established based on the queuing law of vehicles at intersections. Second, the queuing system is put into Simulink for simulation. Delay and fuel consumption are selected as optimization indexes, and a signal timing optimization model is established. Finally, this paper shows applications of three optimization algorithms in optimizing signal timing at a specific intersection in Chengdu. The result indicates that the delay and fuel consumption of the optimized scheme is optimized.

Riding quality affects the vehicle speed, which affects travel time for road users. The International Roughness Index (IRI) is commonly used to describe road roughness. In previous studies, scholars used simulation models to perform regression analysis of IRI and traveling speed. However, simulation models cannot reflect actual traffic conditions and the experiment sample sizes in most studies are small. This paper analyzes the relationship between IRI and free-flow speed through the measured IRI data and traffic data of Shanghai Urban Expressway over three years. The results show that there is a significant linear relationship between IRI and free-flow speed change. For sections with different lanes, the relationship is different. When the IRI is increased by 2 m/km, the free-flow speed of 2-lane section is reduced by 3.2 km/h, and the 3-lane section is reduced by 1.6 km/h.