CICTP 2019: Transportation in China—Connecting the World

As many inland waterways are becoming increasingly busy, it is important and useful to study the vessel traffic flow characteristics to improve inland waterborne transport efficiency and safety by means of traffic simulation. In this study, cellular automata (CA) were adopted to establish a vessel traffic flow model of a variable two-way inland waterway according to inland vessel characteristics, driving behavior, safety requirement, etc. Evolution rules for vessel movement, such as following, overtaking, and accelerating, in the waterway were discussed, where the minimum safe distance between vessels related to their speeds was taken into account. The CA simulation model was validated according to field data regarding the Grand Canal. Based on the simulation, the traffic characteristics of vessel traffic flow are similar to those of vehicles on the road, although these are some differences. The practical simulation model contributes to the understanding of inland waterway traffic flow.

China has expanded many inland waterway locks in recent years to reduce severe navigational delays and improve lock performance. The primary objective of the study was to analyze quantitatively lock traffic performance and to conduct the traffic scheduling using traffic simulation model. The expertise rule-based scheduling rules were developed according to the operation of typical inland waterway locks in China. As a case study, the traffic performance of the Changsha lock in the Xiangjiang River was validated and simulated. The results showed that the load factor of the chamber area, which is related to the arrival distribution, vessel size, and chamber packing, had an obvious effect on lock traffic performance. It may help the operators promote traffic performance according to traffic demand and lock conditions, such as the planned lockage declaration under the cooperation with shipping lines which assigns integrated dangerous goods carriers under the premise of safety.

Inland waterway service areas (IWSA) are an important auxiliary navigation facility of inland water transport in China. The maximum number of vessels that can be accommodated simultaneously is vital for the planning and designing of an IWSA. Taking into account the inland vessel traffic system and service functions, in this article, a simulation model using AnyLogic for estimating the water area of an IWSA was presented and validated based on field data regarding three service areas in the Yangtze Delta. Sensitivity analysis was adopted to identify the impacts of the different distributions and expected values of the driving-in rate and the turnover rate. According to much simulation and regression analysis, the water area could be simplified to a continuous function of the vessel flow rate on the segment. It provides a convenient and reasonable estimation for the required water area of an IWSA.

This paper analyzes the political and economic factors that affect the baltic dry index (BDI) and China containerized freight index (CCFI) based on the fluctuation of these two indexes. By analyzing the volatile trends of BDI and CCFI, and comparing with the recent fluctuations of the Shanghai containerized freight index (SCFI) and China import container freight index (CICFI), this paper summarizes the fluctuation regularity and relationship of CCFI and BDI. During the long-term process of development, the ratio of rise and fall of BDI and CCFI is the same, and the rise and fall of CCFI and BDI are synchronized in about two-thirds of the cases. The purpose of this paper is to provide a basis for shipping enterprises to effectively predict the trend of container freight price and manage freight risks.

In this paper, an analysis method was proposed to investigate the hydrodynamic conditions of navigation near bridge area. Two piers were designed with different spacing and the flow field of bridge area was simulated. Results reveal that the waterway near bridge area is divided into three zones. In the backwater zone, the traverse velocity reaches the first peak with the maximum value; the width of transverse flow around the pier can be calculated by pier Reynolds number, flow Reynolds number, and flow Froude number. In the turbulent zone, the traverse variations of longitudinal velocity, flow Reynolds number, and flow Froude number are the most obvious. In recovery zone, the effects of pier disappear, the traverse variations of flow characteristics are almost negligible. This method has been applied on the Qujiang Bridge. Transverse velocity in backwater zone exceeds the design requirement of 0.3 m/s and the width of transverse flow is 4.22 m.

Cofferdam closing is a key construction process of seawall building projects, analysis based on the fact that tidal level will be practical, and a visualized schedule will help participants communicate easily. This study demonstrates the application of building information modeling (BIM) technology supporting the cofferdam closing schedule analysis. The approach is unique in its ability to forecast the real-time tidal level of the planned construction period and to analyze hydraulic characteristics of the closure process. A combination of BIM technology, including plug-in secondary development and 3D model visualization based on Autodesk Revit, is proposed to distinguish the most dangerous gap condition, and to visually analyze the feasibility of the construction schedule and solutions. The case study of a Yangtze estuary seawall shows that the feasibility and effectiveness of the visualized schedule analysis approach and reveals that it is more conducive for engineers to make reasonable plans and schemes.

The sustainable development of Hainan’s port construction has resulted in a sharp increase in ship traffic density in Qiongzhou Strait, which is impacted by strong wind and waves. Thus navigation safety of ships under different wind scales becomes critical. This paper made an analysis of shipwrecks and the number of various types of ships in service in the research area. Meanwhile, based on meteorological conditions in Hainan, the waves were simulated by using the nested SWAN wave model. After that, combining ALARP with wave height and using risk analysis theory, a risk evaluation for ship navigation security was established. Finally, the risk evaluation model was applied to two directional routes in Qiongzhou Strait. The model could provide the risk situation that ships may encounter when they are sailing intuitively and provide a reference to water traffic safety early warning. The study may reduce the numbers of shipwrecks in Qiongzhou Strait.

The homogeneous development of airports in the multi-airport region hinders labor division and cooperation among airports. This study used similarity coefficient to evaluate degree of homogeneity. Three indexes were chosen to calculate similarity coefficient from three different aspects. The similarity coefficient of each couple of airports under the three indexes is calculated, respectively. A comprehensive weighting evaluation method is applied to evaluate the comprehensive similarity coefficient of each couple of airports based on the value of the three kinds of similarity coefficient, and the homogeneity network of multi-airport in the study region is constructed according to the evaluation results. Based on the complex network theory, the structural characteristics of homogeneity network are analyzed, which correspond to the structural characteristics of homogeneity development of airports in multi-airport region. Finally, based on the structural characteristics and balance on each airport’s gains and losses, this study made three targeted planning adjustments.

In order to improve the spatial structure of regional air logistics networks, the social network analysis (SNA) method is used to construct a quantitative analysis model for spatial structure of multi-value network based on relational data. Using the core-periphery analysis, cohesive subgroup analysis, Bert structural holes analysis, and topological analysis, five quantitative indicators including coreness, constraint, hierarchy, redundancy, and edge correlation is selected to describe the spatial structure characteristics of air logistics network in Jiangsu-Zhejiang-Shanghai area. According to the results, the existing layout takes Shanghai airports as the international hub of air logistics for this area, and uses Hangzhou and Nanjing as the regional distribution centers at both edges of this area. Finally, based on the existing logistics resources, the improvement proposal is put forward that Shanghai, Hangzhou, and Nanjing should be used as the first-level core circle in the regional network and Ningbo, Wuxi, and Wenzhou as the secondary core circle.

To meet the diverse and increasing demand, a comprehensive port usually contains multiple harbor basins and a waterway with multi-junction. This kind of waterway is usually an accident-prone area due to the complicated ship traffic organization in intersections. The traditional traffic organization in a waterway relies on artificial scheduling such as pilots and VTS centers. Few theoretical researches on ship traffic organization that consider safety in intersections have been found. In this context, this paper establishes a navigation simulation model for a port with multi-junction waterways to simulate the whole complex process of ships. The impact of ship speed on navigation safety under different numbers of arriving ships is studied, as various speeds may lead to the different probabilities of collision. The conclusion shows that when the number of ships arriving is different, the influence of different speeds on the traffic organization of the waterway is different, and the conflict rate also has different trends. Our research method can provide a way for the port dispatching department to find out the best ship speed to reach the waterway’s greatest potential considering navigation safety.

With the development of autonomous driving and connected vehicles, active safety systems are increasingly being used for traffic assessment, especially for collision avoidance. These systems identify potential collisions between pedestrians and vehicles by analyzing the pedestrian’s walking behavior. This paper mainly focuses on the crossing behavior and path predicition of pedestrians in group at signalized intersections. First, the group behavior and the intersection context information are integrated by using a dynamic Bayesian network approach. Then a crossing decision model and movement model are proposed to predict the group trajectories in the next few seconds. Finally, real traffic data were collected to calibrate the model parameters, and simulations are applied to verify the model reliability. This model is also conducive for use in autonomous vehicles to drive smoothly and to effectively avoid pedestrians.

Trajectory-based operation is an air traffic control mode with more accuracy, safety, and efficiency, and an effective measure for future airspace management under the conditions of large flow, high density, and short interval, which significantly improves the utilization of airspace resources. Aircraft trajectory prediction is the key technology of trajectory-based operations, requiring the information of high-precision trajectory prediction to achieve high-density operation in airspace. The current state-of-the-art forecasting methods, which perform with low accuracy in low-altitude flight environment, are difficult to be applied in actual operation and management. In this paper, a deep long short-term memory (D-LSTM) neural network for aircraft trajectory prediction is proposed, which improves the prediction accuracy of aircraft in complex flight environments. Multi-dimensional features of aircraft trajectory are integrated into LSTM, and tested on real flight data of ADS-B, demonstrating that the proposed model has higher prediction accuracy than existing methods in different flight phases.

Strategic flight planning is an important part of air traffic flow management. Currently, almost all flight schedules in coordinating airports in China do not satisfy one or more operational requirements, meaning that the distribution of flight schedules is unreasonable, and runway or corridor capacity can frequently not be satisfied, which results in delays. This paper proposes a data-driven strategic flight schedule optimization model to reduce unnecessary flight delays by fine-tuning flight schedules under CAAC guidelines and operational rules. The objective function of the proposed model minimizes operational delay in order to reduce delays while satisfying the airlines’ requests. The model complies with basic CAAC requirements and with corridor capacity constraints determined by historical operational data. The proposed model is applied to two busy double-runway airports: Lukou with four corridors and Xiaoshan with three. Simulation experiments show that the proposed method can reduce delays to no more than 30 min.

Sudden siltation of navigation channels and the resulting impact on navigation safety as well as channel dredging are important issues concerning waterway transportation management departments. For the Guan River Estuary, where typhoons frequently occur, reasonable simulation of typhoon-induced siltation is of great significance. Using curvilinear grids, a triply-nested two-dimensional model which combines typhoon models, hydrodynamic models, wave models, and sediment transport models was adopted in this study. After a series of successful validations, these models were applied to provide a qualitative impression of sudden siltation on this channel during typhoon Damrey. Simulated results show that typhoon Damrey can result in obvious siltation along the channel. It was also found that siltation is unevenly distributed in time and space. By running the nested models, the simulated results can be employed in management of waterway safety and further implementation about channel dredging in this estuary.

At present, ground service vehicle scheduling at most airports in China is still in the stage of manual management. The efficiency of this scheduling method is low, and it easily leads to flight delays. Kunming Changshui International Airport is used in this paper as a prototype. The ferry vehicle scheduling model is established with the objectives of: minimization of the total vehicle number, minimization of the total mileage of the vehicle, and the minimization of the difference in the workload between vehicles. Then we designed a two-stage algorithm for the scheduling problem, based on a heuristic algorithm. The empirical results show that the scheduling scheme can implement intelligent scheduling of airport ferry buses, effectively reduce the number of vehicles required, and the total mileage of vehicles, improve the equilibrium of vehicle workloads, and reduce the cost of ground services.

Replacement of closure segment was first used to solve local failure in a prestressed concrete cable-stayed bridge. Its technical process mainly included removal of the disabled closure segment, reconstruction of a closure segment, and strengthening of the new closure segment. To check thermal effects during construction, the Yonghe Bridge was taken as an example and a finite element model was established. Then typical thermal load cases were considered. Results showed that removal of closure segment led to a modest reduction in the rigidity of main girder, so deflection at midspan, induced by thermal loads, increased notably. Regardless of removal of closure segment, the longitudinal displacement at the end of main girder and at the top of each pylon, caused by thermal loads, were also significant. Therefore, thermal effects should be reasonably estimated in theoretical analysis and variation of structural deformations with temperature should also be clearly identified in construction monitoring.

Prestressed concrete box girder with corrugated steel webs is a relatively new form of bridge superstructure, and the corrugated steel web has been long recognized as the most typical shear carrying member. To clarify the shear behavior of this type of box girder, theoretical analysis, and literature survey are conducted and the latest findings are reviewed, including shear buckling strength of corrugated steel web, shear transfer efficiency of the variable cross-section prestressed concrete box girder with corrugated steel webs, and non-uniformity in the shear stress distribution in different webs of the single-box multi-cell box girder with corrugated steel webs. Based on these findings, it is crucial to establish an effective and efficient shear design method with the aim of promoting the application and development of prestressed concrete box girders with corrugated steel webs in bridge engineering.

To find out the formation mechanism of excessive long-term deflection in a long-span prestressed concrete continuous rigid frame box girder bridge, a finite element method together with an empirical research are used to analyze the long-term deflection in the assistant shipping channel bridge of Humen bridge. The influences of several factors on long-term deflection at midspan in this bridge are demonstrated in detail. Finite element analysis results show that the midspan deflection value induced by concrete creep is limited and could not entirely account for actually observed deflection values in the running period. In addition to concrete creep, equivalent creep effect of traffic live loads applied at the deck, overweight in dead loads, and longitudinal prestress losses are also very significant factors leading to the excessive deflection at midspan. Considering these three factors, the calculated midspan deflection value could coincide with the measured one.

With the advent and development of performance-based earthquake engineering (PBEE), one of the effective means adopted by PBEE is the seismic fragility analysis that combines the seismic demand and capacity models. Tremendous efforts have been devoted to investigate the probabilistic estimate for the seismic demand, while few studies focus on the probabilistic capacity estimate for the widely used reinforced concrete (RC) columns. In order to facilitate the seismic capacity estimate of RC columns in practice, a novel probabilistic analysis method is proposed that can take into account the uncertainties inherent in the structures. This method incorporates the BP neural network, finite element analysis and Monte Carlo simulation, meanwhile the uniform design is performed to improve the quality of BPNN. Finally, the feasibility and accuracy of the proposed probabilistic method is demonstrated through a specific case study. The obtained results show that the capacity index of RC columns has a great variability, thus the random variables related to the structural seismic capacity cannot be neglected in the seismic fragility of RC columns.

The shear connectors embedded in the steel-concrete composite beam are serving to prevent the separation between the steel beam and concrete slab, and take advantage of the material properties of steel and concrete simultaneously. However, the computing methods for the shear connectors seem dissimilar in the existing specifications and standards from different countries. Thus, it is necessary to carry out comparative studies on it. This paper summarized the classical computing methods of shear connectors in the current national norms and industrial standards, followed by a comparison conducted in terms of shear capacity and slip limits through a specific case study. The obtained results indicated that, AASHTO LRFD (2004) requires the minimum quantity in headed steel stud because it does not consider the fatigue and crack issues of shear connectors. This work provides a reference for the design of shear connectors in steel-concrete composite beam.

The Shanghai maglev demonstration line is the first commercial operation line in the world. Many kinds of engineering such as foundation pits excavation, pavement widening, and shield tunnel crossing are constructed near this line. In this paper, a foundation pits project is illustrated and a finite elements analysis software specialized in geotechnical engineering (PLAXIS) is used to estimate the additional deformations of the maglev substructure. It is very important to select the appropriate soil constitutive model in the numerical simulation of foundation pit excavation. Conventional models (Mohr-Coulomb), hardening-soil constitutive models, and hardening soil models with small strain stiffness (HSS) will be analyzed and compared. Also, the deformation results are compared with the preset threshold values in order to provide a reference for the monitoring and protection of maglev line. The abovementioned works will be a reference for the influence analysis of construction projects near the maglev line.

The present study investigates the characteristics of visual structural damages including the hinge joint damage, structural crack, and bearing damage, based on the quantitative inspection of the hollow beam bridges with an average service life of 20 years in Hefei-Wuhu highway. The damage distributions of the hollow beam bridges in the long-term service stage are summarized, and the correlation between different types of damages is analyzed. By selecting a hollow beam bridge with evident structural damages based on the visual inspection, the vehicle loading tests under multiple load conditions are carried out to determine the capacity of loading distribution along the lateral direction. Through data mining, the statistical analysis between the visual structural damages and lateral loading distribution is conducted. It can be concluded that the internal condition of hollow beam bridges can be evaluated by the visual inspection results of structural damages.

This paper proposes a technical solution for the reinforcement of reinforced concrete beam bridges with steel plate-MPC (modified polymer concrete) composite. MPC composite is a polyurethane-based high-strength lightweight material with good adhesion to concrete and steel, and has high tensile strength. Steel plates not only work together with MPC as the reinforcing material, but also serve as the molds of MPC pouring. The combined reinforcement of both can achieve the effect of rapid construction and improvement of bending bearing capacity. The finite element software ABAQUS is used to analyze the reinforced concrete rectangle beam strengthened by the steel plate-MPC composite strengthening. The parameters of steel plate thickness and MPC material usage are analyzed. The improvement of the bearing capacity and the failure mode of the main beam are analyzed. The research results can provide a new method for bridge reinforcement.

We carried out comparative research on anti-fatigue design methods in AASHTO, Eurocode, Japan bridge type book and Chinese code for the Nanjing Yangtze River Third Bridge. We comparatively analyzed the similarities and differences of four codes on the fatigue safety criteria, the fatigue strength grade division and the standard fatigue model. The results show that: ① Eurocode and Japanese code contains the idea of cumulative damage design, although the calculation is large but more realistic; ② for fatigue strength grade division, Japanese and American code is similar, the Eurocode is the most comprehensive and meticulous, while Chinese code is based on Eurocode and the classification of fatigue details needs to be further improved; and ③ there are some differences in axle weight, wheelbase and landing area of standard fatigue model in four codes. Chinese code axle weight is the same as Eurocode which has the largest fatigue effect, and the engineering application is safe.

Orthotropic steel bridge decks are used in long-span bridges due to their structural characteristics. The orthotropic steel bridge decks will have staggered deviation during their assembly process, and stress concentration occurs at the staggered position. We study the fatigue performance and life of the top slab with assembly deviation of the orthotropic steel bridge deck structure. Research is carried out to reduce local stress due to assembly deviation. The results show when the longitudinal joint dislocation on the top slab is welded with slope, the greater the gradient of the weld, and the greater the stress around the weld. When the transverse joint dislocation of the top deck is directly butted, the stress concentration on the lower surface of the joint dislocation is obvious. To reduce fatigue stress, it is feasible to pave 2 cm MPC (modified polymer concrete) composite material above the top slab and then pave asphalt concrete.

Polyurethane fly-ash (PUFA) composite material is a new type of reinforcement material. It has the advantages of high strength, light weight, and good adhesion to concrete. It has been applied in bridge reinforcement in recent years. The interfacial adhesion between PUFA and concrete is a key factor in the success or failure of reinforcement. In this paper, the shear-peeling behavior of the interface between PUFA material and concrete is studied experimentally. The test was designed by orthogonal test design method, and the test piece was made into 25 in-plane shear-peeled test pieces. Through the interface shear peel test, the bond strength, and failure mode of PUFA-concrete interface layer structure and in-plane shear were obtained, and the bond slip curve was obtained based on the load slip curve by fitting method. The research results provide a reference for PUFA engineering applications.

Using mounted video camera to collect pedestrian-crossing behavior at intersections, this study investigated the behavior characteristics and the corresponding impact factors. A pedestrian-velocity extraction method based on video images was proposed using perspective transformation, with which the descriptive statistics and spatial distribution of pedestrian-crossing speed were derived. The impacts of age, gender, population density, length of pedestrian crosswalk, and crossing-timing on pedestrian-crossing speed were also investigated. Finally, implications of this study for design speed of pedestrian crosswalk were discussed. Based on 940 intersection-crossing pedestrians from 10 intersections, the results showed that the average intersection-crossing speed was 1.45 m/s; pedestrians had larger speeds in the middle part of the crosswalk; elder pedestrians had the slowest crossing speeds; male pedestrians had faster crossing speeds than females ones; pedestrians had slower crossing speed when they were in larger groups; and the proper design speed for crosswalk was 1.1–1.2 m/s.

In view of the mass of accidents currently caused by distracted driving, research on distracted driving recognition methods was conducted. Driving simulation tests were carried out in which drivers were driving normally, were using a hand-held answering phone, and were using a hand-free answering phone during the lane change process. A radial basis function neural network model based on orthogonal minimum square method for distraction detection was built. Results show that drivers needed more time to complete a lane-change process and made more operation mistakes when he is answering a phone. Six driving performance parameters as vehicle’s longitudinal velocity, lateral velocity, lateral acceleration, steering angle, steering wheel velocity, and opening degree of accelerator, changed significantly and were chosen as input variables for distraction recognition model. The average recognition accuracy of the model reached 88.7%, which could accurately identify if a driver is distracted in real time.

This study has two essential purposes: First, to develop a simplified cycling behavior questionnaire (SCBQ) for Chinese roads. Second, to verify the validity of the SCBQ constructs. A total of 338 valid questionnaires have been obtained in a group of college students. The competitive confirmatory factor analyses (CFA) is used to obtain basic psychometric values, and an optimized structure for the scale has been established. The obtained results suggest that SCBQ has a clear factorial structure, items with high factorial weight, and good internal consistency. The findings of this research develop a SCBQ method, which has good validity in assessing the cycling behaviors for Chinese University. Furthermore, the results may have application in the improvement of cycling safety.

With the development of smart phones and intelligent drive aiding devices in cars, it’s more likely for drivers to have serious distracted behavior while driving. Distracted driving is the most dangerous driving behavior, and a hot issue in the field of road traffic safety, which calls for further research. Therefore, questionnaires regarding distracted driving behavior were designed and analyzed statistically so that the reliability and validity of the questionnaires could be tested. The results showed that the questionnaire had good reliability and validity, and that it could be used as an effective tool to evaluate distracted driving behaviors. In the process of verifying the construct validity, a three-factor construct scale of distracted driving behavior was explored and validated, and then a structural equation model was established.

This paper considers a comprehensive naturalistic driving experiment to collect driving data about vehicle’s lane change behaviors on an actual Shanghai highway. Using the acquired real-world natural driving data, a lane-change database with kinds of vehicles’ driving information is established. A novel way of selecting variables is proposed by considering the values and changes of various variables over time periods. The random forest (RF) and Gini coefficient are adopted for variables’ importance ranking, and a total of seven top contributing factors are selected. The results indicate that the variables concerning vehicles’ acceleration and speed, especially the standard deviation of longitudinal acceleration, exerts the greatest influence. Subsequently, based on these key factors, the long short term memory (LSTM) is utilized for performing training and predicting operations on our collected naturalistic driving data. Finally, good effects and high accuracy on the recognition and extraction of lane change behavior are obtained.

The purpose of the study was to examine the impacts of the driving duration and circadian rhythm on the vigilance level of the drivers and to identify the correlations between the vigilance and driving performance. Ten drivers performed a nearly 200-km motorway driving session on a driving simulator. The quantification of physiological vigilance level was evaluated using electro-encephalogram (EEG) and electro-cardiogram (ECG) recordings. Driving performances were measured using reaction time (RT) and standard deviations of speed (SDS). The results showed that driving duration had significant impacts on the drivers’ vigilance and driving performances, and a 2-h driving task had already decreased their vigilance level and deteriorated their performances. The impacts differed significantly by the initial mental states of the drivers. A certain time of rest was advised for all the drivers when they had already been driving for 2 h, which might reduce the probabilities of the traffic accidents.

Mixed control by driver and automated system is going to be implemented for decades until fully automated driving is achieved. Thus, the driver has to re-control the vehicle accurately and in a timely manner when an automated system sends a take-over request (TOR) at its limitation. Workload is the actual capacity required for a person to complete a specified task. While in driving task, excessively low and high workloads result in an insufficient capacity of drivers. Hence, a driving simulator experiment was performed in three levels of automated driving conditions, with 31 participants involved. The results showed that compared to manual driving conditions, a driver’s subjective workload was significantly reduced in both partial and highly automated driving conditions. The results support the application of an automated driving system, as it reduces driving workload. The results also supported that participants reflected higher workload when encountering more critical situations, especially under higher levels of automation.

From temporal and locational perspectives, the influence factors of traffic violations for taxi drivers remain unclear. Hence, this study attempts to investigate the effects of temporal and locational factors on traffic violations of taxi drivers using descriptive statistics and Chi-square test based on data extracted from the off-site enforcement camera system during the year 2016 in Luzhou, P.R. China. The results show that the most common traffic violation of taxi drivers is “use of an improper oriented lane”; season, day of week, time of day, road type, and road segment have varying influences on traffic violations of taxi drivers. Apart from speeding that mainly takes place on the secondary highway in the urban settings, the other traffic violations largely happen on the urban road. The findings of this study imply that traffic supervisions and interventions specifically proposed for traffic violations of taxi drivers should consider the appropriate time and location.

Path following controller has played an important role in autonomous driving since it directly affects the driving quality as well as the comfort level of vehicle riders. Previous path following controller faces the difficulties of dynamics model building and parameter tuning, and is not able to mimic the driving style of human drivers. Therefore, this paper proposes a data-based trajectory following controller, which is a model-free controller, and the parameters are self-learned from naturalistic driving data. Moreover, the controller learns the driving style of the experimenters, which make the vehicle riders more comfortable. Naturalistic driving data is collected on a HAVAL H7 vehicle equipped with GPS-RTK system. Deep learning technique is applied to generalize the driving data and then generate the controller. To validate the performance of the path following controller, simulations are conducted using CarSim and MATLAB/Simulink. The results indicate that the controller has a strong ability of generalization, and is able to finish the test scenarios at a high driving quality.

In order to improve the accuracy and speed of driver fatigue detection, in this paper we propose a new algorithm based on cascaded convolutional neural network for driver fatigue detection. First, we design a lightweight multi-task cascade convolutional neural network called LMTCNN for face detection and eyes key points positioning. Then, according to the key positions of the eyes, images of eyes can be captured. We convert the images to HSV color space and complete pupil positioning in H channel. Finally, ellipse fittings are used for obtaining pupil contour. The ratio of ellipse long axis and short axis can indicate the opened/closed state of eyes. We count the time of eyes closure during a period to calculate the PERCLOS and further judge the fatigue status of driver. Through comparative experiments, we demonstrate our algorithm outperforms mostly current driver fatigue detection approaches in both accuracy and speed.

Even if the urban rail transit facilities have a high degree of automation and automatic driving technology is becoming increasingly sophisticated, it is still necessary for train drivers, station operators, station attendants, and other workers to carry out operations and production in the front line. The rail transit driver is an important part of rail transit operations. In order to ensure the safety of the operations, it is necessary to monitor the rail transit drivers’ physiological status and operating conditions. The system uses the photoelectric volume pulse wave tracing method to obtain the pulse wave data of the urban electric train driver for pulse variability analysis. Taking the portable bracelet as the main body, a pre-post and online physiological condition monitoring system dedicated to the urban electric train driver is designed. Through active monitoring and active prevention, the safety hazards in operational production of the drivers are reduced.

The current research on the threshold value of slow traffic is not in-depth enough. The studies are mostly related to physical strength, and hardware facilities, which usually ignores the psychological feelings of participants. A questionnaire survey has been conducted in the old city zone, and the threshold value of walking distance, bicycle travel distance, crossing distance, and red light waiting time was studied by using the Kishi’s logit price sensitivity meter (KLP) method. Meanwhile, the reasonable threshold value in line with the old city zone residents travel habits and travel psychological is researched. This value provides a large reference for guiding the planning and design of the slow-moving system in the old city.

The driver is an important component of the driver-vehicle-road closed-loop system, and there are great differences in vehicle handling by drivers with different driving skills. Compared with novice drivers, skilled drivers have a more detailed understanding of vehicle dynamics and more accurate perception of vehicle status. However, the individual differences between drivers can’t be expressed in existing driver models in detail. Therefore, it is necessary to establish driver models which can simulate drivers with different driving skill in order to quantify the man-vehicle-road closed-loop system more accurately. This paper presents three driver models which distinguish drivers from three aspects: preview characteristic, reaction ability and their understanding of vehicle dynamics. To verify the validity and superiority of the driver model above, the path tracking simulation is carried out using Carsim/Simulink. The simulation results show that the proposed model has a better performance compared with the existing driver models.

In order to accurately depict the driver’s behavior of choosing the driving speed under uncertain conditions, the paper uses the prospect theory in the field of psychology model, and studies the mental process of speeding behavior to complete the traffic speed selection behavior model. The model is based on the prospect theory and simulates the alternatives under different scenarios. The calculation results based on the expected utility theory and the prospect theory show that the prospect theory is more consistent with the driver’s driving behavior when describing the traveler’s choice. Therefore, studies of speed selection behavior based on the prospect theory provide a new idea for the research of traffic safety and traffic management in future studies.

At freeway interchange diverging areas, traffic conflicts are caused directly by driving behaviors. This study aimed to investigate the correlation between traffic conflicts and driving behaviors, which include acceleration/deceleration and lane changing at diverging areas based on Chinese cases. Principal component analysis was used to make a dimension reduction of the parameters that influence traffic conflicts. The Bayesian network was used to construct a correlation model between parameters and traffic conflicts in diverging area. A significant correlation existed between the lane-changing position and the traffic conflicts, which were located within 600 m from the beginning of the diverging area and the first 60 m of the deceleration area. A significant correlation existed between acceleration/deceleration and the traffic conflict. Deceleration and lane-changing behavior are the main factors that affect traffic conflicts. The research provided useful advice for driver training programs to reduce casualties and a basis for the active prevention of accidents.

With the increase of the number of vehicles, cruising for curb parking in the central business district is obvious. Cruising for parking not only consumes time, but also affects normal driving. Through the questionnaire and video survey of cruising mode for curb parking carried out at Youshige Business District in Beijing, this paper analyzes the phenomenon of cruising for curb parking. Based on the survey data, a binary logit (BL) model of cruising mode for curb parking is constructed. This model analyzes the influence of different factors on the decision of cruising mode for parking. The results show that vacancy dispersion coefficient, vacancy location coefficient, and other factors have significant impact on the decision of cruising mode. Setting different prices at different parking sections and timely publishing of parking space information can improve parking efficiency and promote normal traffic.

The departure curbside is an important part of the airport. This paper establishes two different curbside pedestrian delay models and proposes the level of service at the departure curbside based on pedestrian delay. If the level of service is low, the optimal signal distribution is determined using Vissim simulation; the pedestrian delay time is reduced and the level of service at the curbside is improved. This paper analyzed the Beijing Capital International Airport as an example. The airport was given the optimal signal times during the peak hours and improved the level of service at the curbside. This paper evaluated pedestrian delay and analyzed the time sequence of pedestrian crossing signals at the departure curbside, which has a certain practical guiding significance for traffic management and control. It provides a theoretical basis and foundation for the pedestrian service level evaluation method system and pedestrian transportation planning and design.

With the construction of expressways in cities, the ramp merging bottlenecks have attracted much attention, which restrict the capacity of expressways and affect traffic safety. In this paper, traffic data of upstream section, merging section, and downstream section of the bottleneck in Yingtian Avenue in Nanjing, China are collected and analyzed as an example. The bottleneck characteristics are studied from the perspectives of both macroscopic traffic flow and microscopic vehicle behaviors. The traffic factors affecting the bottleneck are investigated as well. Based on measured traffic data from video observation, characteristics of mainstream flow and merging flow are analyzed on a macroscopic and microscopic level. The relationship between traffic parameters is analyzed to study characteristics of the bottleneck, and three equations are worked out to describe the relevance.

Sight is poor in the curve which will cause difficulties for drivers to determine road conditions accurately (Farid I. Kandil & AlexanderRotter, 2010) leading to traffic accidents. Throughout the driving process, visual characteristics play a key role. Based on the research at home and abroad, this paper analyzed driver’s gaze characteristics in curved sections. We designed a simulation experiment collecting 15 drivers’ eye movement data. Driving simulators recorded the movement data of vehicles. We analyzed the relationship between several time indicators and radius of curve and the influence of vehicle’s manipulation and assessed dangerous area in curves. Results showed radius of curve has influences on previewing time, continuous gazing starting relative to curve starting and continuous gazing ending relative to curve ending. Most drivers will stop gazing before exit the curve, so this stage has a high risk of accidents. Moreover, gaze characteristics also influence vehicle’s manipulation.

Influenced by the closed and monotonous environment inside long highway tunnels, drivers are prone to be irritable and unconsciously accelerate to leave the tunnel, which exerts adverse influence on driving safety. To alleviate the psychological pressure of drivers, this paper, based on theory of color psychology, built numerous highway long tunnel models with different colored sidewalls, after which numerous driving simulation experiments were conducted to research the influence of sidewall color on driver’s heart rate and speed. The results showed the growth of heart rate would increase when drivers entered the long tunnel at about 1,500 m. Considering the color of the sidewall, the mean growth of heart rate was smallest in cyan, and the standard deviation of speed was smallest in blue. Considering color combinations, the mean growth of heart rate and the standard deviation of speed were smallest in the combination of blue and white.

The change of light intensity at the entrance of a tunnel, which causes the driver’s visual lag and the increase of reaction time, will affect the driving safety. Therefore, it is necessary to study the visual characteristics of tunnel drivers and put forward corresponding lighting optimization measures to improve the safety of tunnel operation. The illumination values at different sections of the tunnel entrance were collected during the vehicle test and the Sigmoid model of illumination changes at the tunnel entrance was constructed. Based on the Sigmoid model, the pupil area and illumination were analyzed by statistical methods and the visual adaptation model was established by polynomial-approximated approach. Finally, according to the present situation of tunnel lighting, optimization measures for transportation safety were put forward.

At present, the focus on the law of drivers’ eye movement characteristics in the aspect of individual differences has not been developed. It has a certain influence on the applicability in the research of traffic safety and evaluation index. In view of the issue above, this paper systematically studied the law of drivers’ eye movement and the characteristics of individual differences based on driving simulation and the eye movement tracing platform. The eye movement information was collected through the simulated driving test, and then it was processed and analyzed. Further, quantitative analysis was used to explore the impact of individual differences on the test, and clearly obtain the groups which adapt to different research demands. In addition, it will provide the basis for the selection of subjects for the study of eye movement characteristics and the individual selection of post-experiment analysis.

In today’s rapid developments of intelligent transportation, voice navigation has been widely used. In order to reduce the risks of traffic safety caused by drivers’ delayed reactions to voice prompts at expressway exits, a simulated driving experiment was designed to study a driver’s auditory reaction characteristics during lane changes. A total of 90 experiments were conducted on 30 participants using a driving simulator, and experimental data was collected by D-Lab software to measure auditory reaction time. Measurements begin at the start of voice prompting for a lane change and the time point at which the angular velocity of steering wheel changes the most is the ending point. The experimental data is fitted with a normal distribution to obtain the expectation parameter of a driver’s auditory reaction time. We conclude that driving proficiency has the greatest influence on the driver’s auditory reaction characteristics.

Understanding avoidance driving behaviors is important for both drivers and traffic managers owing to the high correlation between accidents and human driving behaviors. The most recent research in this area considers acceleration as the implement of decision result, which may cause inaccurate prediction results as acceleration changes continuously. To solve this problem, this study considers the acceleration as a continuously changing variable and utilizes jerk, the change rate of acceleration/deceleration to quantify the change of acceleration. We further applied the jerks in a game theory vehicle avoidance model. This method analyzes driving behaviors in a more detailed magnitude and finally improves the model results. Finally, a case study shows that this work improves the prediction accuracy of the driving behavior model compared to previous research.

The head-up display (HUD) is a display device which projects a virtual image on the forward field of view of drivers. It was first developed for military aviation then started to be applied for cars. Great progresses have been made to HUD in research and practice. A good summary of the current work regarding HUD will facilitate its future applications and research. This paper provides a detailed review of the key aspects associated with HUD. First, the conception of HUD and its various categories were introduced. Second, HUD systems by different manufacturers were reviewed and compared, showing the current field applications of HUD. Third, the academic research mainly on driving behavior related to HUD is summarized. Last but not least, the future direction for automotive HUD is also provided to show its great potential.

Fatigue driving is considered one of the main risk factors contributing to truck accidents in transportation. Most of the existing studies, however, focus mainly on private car drivers, and in most cases, use driving simulators. To fill the gap of field-test driving studies focusing on truck drivers, in this study, we present the results of field tests of heavy-duty truck drivers’ driving performance at signalized intersections when the drivers are affected by fatigue. Kinematic driving parameters were collected as measurements of drivers’ driving performance, and the percentage of eye closure (PERCLOS) was collected to estimate drivers’ fatigue levels. The results indicate that drivers with higher fatigue levels tend to pass signalized intersections with higher speed but lower stability. Moreover, due to drivers’ fiercer actions and slower reactions when they are tired and off guard, accidents are more likely to occur at signalized intersections, where the traffic condition is really complex.

Car-following behavior is a basic micro-driving behavior. Previous researches have been devoted to modeling car-following behaviors in western countries, with few studies focused in China. Shanghai naturalistic driving study (SH-NDS) is China’s first naturalistic driving study, which provides high-precision microscopic driving behavior data. Based on SH-NDS, this paper calibrates and validates IDM and the Wiedemann model. Results show that IDM is calibrated with a high and robust performance, justifying its suitability for microscopic traffic simulation. Fundamental diagrams for IDM show that on freeways: (1) density is slightly above 40 veh/lane and flow reaches its maximum value close to 2,200 veh/h/lane; (2) jam density is about 150 veh/lane and free-flow speed is close to 100 km/h; and (3) optimal speed, related to maximum flow, approaches nearly 55 km/h. This study reveals the Chinese drivers’ driving behavior characteristics on the freeway and can contribute to the international development of car-following theory and its various applications.

Based on the prospect theory, this paper analyzes the decision-making process of travelers’ travel mode under bounded rationality. A travel mode choice method which is more in line with human thinking habits is proposed, and travel decision model based on travel time cost, travel cost, and travel comfort cost is constructed. The reference point of the travel mode choice behavior model is given, realizing the comprehensive prospect value calculation of each trip mode. Through a simple example, the travel mode selection model proposed in this paper is applied to calculate and analyze the six different travel modes of bus, private car, taxi, shared bicycle, urban rail transit, and walking. The results show that the travel mode choice model under bounded rationality is more consistent with the traveler’s limited rational decision-making process, which provides a new way to predict traveler’s travel mode choice.

This study presents a prospect theory model, focusing on electric vehicle (EV) drivers’ choice from a set of charging stations. The research consists of three parts including driver classification, model construction and model validation. Based on the state of charging (SOC) and charging time, the EV drivers are divided into three categories and these three types have different risk propensity which affects drivers’ choices. The prospect value is calculated according to the drivers’ categories, the detour distance, charging cost, and time cost. The proposed model is validated by a small scale case study in Shanghai. A useful method to determine the reasonable alternative charging stations is also provided to avoid time-consuming searching among all stations in Shanghai. The parameters are also calibrated according to the large scale case study.

Drivers are likely to overestimate the distance to children crossing streets because the drivers mistake children for adults, especially under poor light and in a monotonous visual frame of reference. We conducted a quantitative study on the correlation of distance, pedestrian height, and motor vehicle speed for studying driver distance identification rules on children crossing streets at night. We calculated the overestimated distance between vehicle and pedestrian and used that analysis to calculate speed limits of the road section. The results indicate that when the height of children is 115 cm (five percentile height of school age children) and the driver speed is less than 35 km/h, the overestimated distance is less than 11 m. Ultimately, we suggest setting a 25 km/h night speed limit, providing reflective clothing, and improving crossing facilities in school areas.

This paper analyzes the potential psychological and physiological effects of heavy vehicles on the driver in the rear vehicle at basic freeway segments. First, we define the influence of the heavy vehicle on the driver in the rear vehicle. Then we studied the basic features of the influence on drivers through SP surveys. Finally, we used binomial logistic regression models and fuzzy logic to screen out significant factors and construct the calculation model for the heavy vehicle influence, respectively. We found that the influence of heavy vehicles can change the driving behavior of the rear vehicle. Moreover, passenger car drivers are more sensitive to the influence than heavy vehicle drivers. The proposed model can calculate the influence degree of the heavy vehicle quantitatively, where the factors vary for passenger car and heavy vehicle drivers.

To evaluate the effects of driver fatigue on the autonomic nervous system (ANS), Poincaré plot indices of heart rate variability (HRV) were investigated. A total of 20 volunteers were recruited to perform a 60-min duration driving experiment on a high-fidelity driving simulator with ECG recorded. The Poincaré plot derived from RR intervals of ECG was quantified by measuring SD1, SD2, SD2/SD1, and S. The results showed that all four indices increased significantly (Mann-Whitney U test, p < 0.01) in fatigue state compared to an alert state. Poincaré plot indices seem able to distinguish drivers’ mental state and have potential prospective in ECG based driving fatigue detection systems.

Countdown-type traffic signals for vehicles that display the remaining green time and waiting red time have been installed positively in most Chinese cities. In this study, we analyze the behavior of vehicles under the countdown signal, especially the behavior of vehicles moving when the traffic signal changes from red to green. First, a variety of data were collected through observation of vehicles in Shenyang using video recording. Moreover, the distribution diagram and startup delay model were constructed according to processed data. The results show that countdown traffic signals would shorten startup delay, and work on the third car in the queue, but could also lead to a certain percentage of premature starts. On the whole, it appears that countdown signals can improve traffic efficiency and traffic safety at intersections.

By collecting real-world data of 2,500 battery energy vehicles (BEVs) in Shanghai, China, this paper presents the distributions of summary metrics describing the travel patterns and charging behavior among large-scale electric vehicle owners. It adopts a cluster approach DBSCAN to identify owners with heterogeneous usage of BEVs in terms of six metrics and segments them into three types. It is found that: (1) majority of single travel distance are short and possible range anxiety exits; (2) the majority of BEVs owners spend 2 or 3 h on single charge while part of them spend significantly more time at charging than required, which suggest the waste of charging facilities and resources to some degree; (3) three types of owners can be identified according to their heterogeneous usage of BEVs—that is, ride-hailing drivers, commuters, and other-related drivers; (4) the result would suggest a coupling relationship between travel patterns and charging behaviors—that is, shorter single stay time, more number of stay events, and higher travel distance in peak period correspond to earlier start time of charging and more number of charging events; and (5) the proportion of ride-hailing drivers is 44.07%, which is relatively high among BEVs owners, which can be explained by the new regulations published in Shanghai and the lower marginal cost of BEVs in ride-hailing services.

In actual traffic safety studies, it is important to consider the differences between a straight and a curved road. We put forward a calculation method of inter-vehicle distance on the curved road in the V2V communication environment. Next, the safety margin (SM) is introduced to characterize the driver’s hazard perception level and the calculation method of the curve SM is proposed. Data under urban curved-roads conditions in Beijing was collected by real vehicle experiments. According to the results of data processing, the variation rule of SM on curved road was discussed. When the vehicle turns at the unsignalized intersection, the change trend of the SM is first decreased and then increased; when the vehicle turns at the signalized intersection, the change trend of the SM value is slowly increased. This study can be applied in curve speed warning systems and adaptive cruise control systems to improve driving safety and comfort.

Driving behavior is one of the important causes of traffic accidents, and has always been an important research hotspot in the field of traffic safety. Using existing highway traffic laws and regulations, this paper uses a virtual reality platform to simulate the real driving environment to test the driver’s reaction time on the highway, and to evaluate and predict the driver’s driving behavior reasonably.

Distracted driving is an important factor affecting the safety of road intersections. However, the research on driving distractions mostly focuses on high-speed situation and closed sections, and there has been a lack of research on low-speed situations and open sections. Meanwhile, there are few studies on crash or near-crash scenarios. Therefore, the paper chose four kinds of typical intersection conflict scenarios to study the effects of the two most common driver distraction: auditory-cognitive distraction and visual-cognitive distraction on drivers’ perception and reaction to traffic conflicts. Based on the driving simulator, four kinds of typical intersection conflicts were set up and 42 people participated in this experiment. This paper explores the effect of different kind of driver distractions on drivers’ perception and reaction to traffic conflicts using the multi-factor variance analysis with the data collected by an eye tracker, driving simulator, and physiological equipment.

Continuous compaction detection method was investigated. For the monitoring, an embedded-based detector was designed. For the trajectory, a collaborative positioning method was proposed. For the effects, harmonic analysis was introduced. Positioning experiments were conducted and the accuracy was improved to 0.48 m. Rolling tests were performed with three passes, and for the whole area, the average compaction meter values (CMVs) were 98.22, 139.21, and 215.45. The average compaction control values (CCVs) were 103.05, 155.45, and 196.68. Thus the trends of CMV and CCV changing were consistent with rolling. Combining with monitoring video, CMV was more sensitive to irregular operations. For the specific section, the average multiples of CMVs were 1.42, 1.56, and CCVs were 1.51, 1.33. Thus, CMV was more consistent with lower dispersion, and was selected as compaction degree parameter. The results showed that continuous compaction detector meet the monitoring requirements at low-cost, and could lay foundation for intelligent compaction.

A new collaborative positioning technology based on global positioning system (GPS) and ultra-wide band (UWB) system was proposed for pavement construction monitoring. The civil GPS system was used to track rollers for numbers of passes; the UWB system was used to acquire the locations of rollers for the positioning of rolling. An optimization algorithm based on Kalman filtering algorithm was developed to improve accuracy. The positioning experiments were carried out and the results showed that the accuracy of the civil GPS module could be improved to 1m, but was still not enough for the positioning of rolling. The accuracy of the UWB system reached 0.1 m, but the ranging area is limited. The collaborative system improved accuracy to 0.48 m with a large ranging area. Taking advantages of both subsystems, the collaborative positioning technology meets the requirements of construction monitoring at low-cost. This research could lay the foundation for continuous compaction control.

With the rapid development of roads in China, highway mileage has increased. Highway management departments spend a lot of money for road maintenance every year, but the growth of funds cannot keep up with the increasing demand for maintenance. Highway management departments need to find new ways to carry out the maintenance. To establish highway maintenance, planning is considered to be the best choice for all countries. In this paper, the processes and decision making model for highway preventive maintenance planning are determined. The method of preventive maintenance planning provides a scientific guide for maintenance management of highway maintenance and management department. This method is applied to the 10 year highway preventive maintenance planning in Hebei Province.

The construction process of asphalt pavement is complex and interrelated. The existing specifications are not strict in the gradation control during construction process and have poor pertinence, which results in difficulty to achieve an effective control. To solve these problems, this paper proposed the ratio corrected coefficient of variation for original aggregates to evaluate the variability of aggregates based on the practical project. At the same time, the gradation of asphalt mixture in the whole construction process was analyzed by means of tracking observation. The results show that the index can well-characterize the variability of aggregates. The most sensitive process of variability and the gradation change in the continuous construction process were obtained. It was suggested that the sensitive process should be taken as the control focus in the follow-up construction.

This paper proposes a matter-element theory-based method to determine the maintenance priority of road sections for network level pavement maintenance decision-making. The method develops a multi-index and multi-objective model capable of incorporating all potential pavement maintenance related factors. A total of five pavement maintenance related indices were considered in the study, including pavement overall quality, pavement structural capacity, pavement age, traffic level, and road functional class. The five indices were normalized to the same scale of 0–100. The weighting values of the five indices were also determined through expert consultation. The matter-element analysis process was conducted to determine the comprehensive correlation degree, which indicates the overall ranking of a road section in the network level decision-making. We clearly demonstrated the applicability and rationality of the matter-element model based decision-making method and can be used as a guideline for pavement maintenance agencies.

Traditional highway maintenance and management systems treats its own main functions and pays less attention to design, and construction stages. This results in weakening of the whole engineering cycle efficiency. With the rapid development of building information modelling (BIM), there comes a natural application in highway maintenance and management area. This paper presents an approach to rebuilding a highway information model in Autodesk suites and proposes a method to implementing BIM technique to highway maintenance and management. Result shows that a highway BIM model could be rebuilt efficiently and could be used as a tool for highway maintenance and management. In addition, requirements of modeling a highway and essential points to manage the operation of highway on a BIM-based platform are put forward. It could also be concluded that BIM technology will be further used in the whole engineering cycle in highway engineering.

The driving environment of expressway maintenance work zones is complex and the rate of accidents is high. It is very important to improve road traffic safety, evaluate driving risks, and establish risk assessment models in maintenance work zones. In this paper, driving simulation experiments are used to analyze four factors of driving risk in the working area: vehicle flow density, control measures in maintenance work zones, alignment of road segment, and position of closed lane. Then the maintenance work zones are divided into three areas: warning area, interleaving area (confluence area and shunt area), and working area. A multiple regression model of driving risk in different areas is constructed. Finally, research objects are selected to verify the risk evaluation model with real vehicles, and the deviation between risk prediction value and risk actual value is about 7% and 9%, which proves that the risk evaluation model is effective.

The influence of rainfall on slope geological hazards was analyzed, the method of effective rainfall was improved, and the impact of rainfall on slope was classified based on the effective rainfall. Combined with the characteristics of geology, climate, rock mass structure, earthquake, and slope protection of the highway in the Yunnan Province of China, the safety risk assessment index system and grading criteria of soil-similar slopes consisting of strongly weathered soft rock and extremely soft rock soil were established under the conditions of strong rainfall and high intensity earthquake, in which the sources of risk such as the scale of slope, geological conditions, deformation and destruction, surrounding environment, and inducing factors were taken into account. The reliability and feasibility of the index system and grading criteria were verified through during the engineering of the second-grade highway from Lancang to Menglian to Meng’a in Puer City, Yunnan Province, China.

This study aims to investigate the effect of corrective treatments on the life cycle of pavement. An optimum analysis is conducted by using the genetic algorithm to find the optimal M&R plan during a 10 year analysis period for each pavement sector where the objectives are minimizing the total costs and maximizing the average performance throughout the 10 years. Two maintenance policies (MPs) are investigated and compared together; MP1 where the rehabilitation and preventive treatments only are allowed along the 10 years; and MP2 where unlimited application of corrective treatments are allowed besides the rehabilitation and preventive treatments. Moreover, sensitively analysis is conducted to investigate the effect of corrective actions at different weights assigned to the policy objectives. The analysis is conducted at weights (0.2, 0.8), (0.5, 0.5), and (0.8, 0.2) for cost and performance respectively.

In order to improve the rutting resistance of expressway asphalt pavement, a hard asphalt additive compound, EME-14, was used to prepare the high modulus asphalt mixture. Using the rutting test and dynamic modulus test at different temperatures, the high and low temperature properties of the EME-14 and common high modulus asphalt mixtures EME-14, SUP-20, and SBS modified AC-20 asphalt mixtures were compared and analyzed. Predictive analysis was carried out by using the dynamic modulus main curves. The results showed that the dynamic stability of high modulus asphalt mixture EME-14 prepared by hard pitch additive compound was 11,059 times/mm, which was about 1.4 times of EME-14 dynamic stability of common high modulus asphalt mixture, 1.6 times of SUP-20 dynamic stability, and 1.8 times of modified AC-20. Compared with traditional middle-layer asphalt mixture, it has excellent rutting resistance performance at high temperatures.

With the development of global transportation infrastructure, the transportation field has transferred its focus to transportation infrastructure asset management (TIAM). The optimized strategy design for TIAM has been studied by many more researchers and scholars recently, and their common focus is how to realize different benefits at the same time. Through the analysis of strategies data and the current methodologies in the field of optimization, this paper established a model with multiple objectives, solved by a genetic algorithm (GA), and improved the GA to provide a theoretical method. It then uses a North American City Road network as an example. The results show that this model is efficient to the network-level TIAM optimized strategy design, which would contribute to the TIAM work in other countries and areas around the world.

This paper aims to analyze the influence of physical parameters of asphalt pavement on ultrasonic waves and tries to establish the relationship between physical and acoustic parameters. Among them, physical parameters include the density, elasticity modulus, viscosity, and incident frequency. Acoustic parameters consist of amplitude, frequency, and attention rate. Based on finite element software COMSOL, both mechanical and acoustic models were established to simulate ultrasonic propagation in asphalt pavement. Through time and frequency domain, it was found that with the increase of distance, amplitude has an exponential reduction. Meanwhile elasticity modulus and viscosity had a high sensitivity to acoustic parameters, and others were weak.

Based on HIPERPAV software, six curing methods were brought to bear on concrete pavement surface, such as liquid curing agent, PE film, and burlap. Then, the effect of curing methods on development regularity of flexural-tensile strength and maximum stress within 72 h was determined for its curing problems in early stages. The results showed that the stress strength ratio of cement concrete slab reached maximum after 13 h from paving. In this case, cement hydration was a key factor for stress increasing dramatically. Then, the stress strength ratio changes coincide with ambient temperature changes because cement hydration did not play the leading role. The interval 13–24 h after paving was a high risk period because of early cracking on the surface. In this period, hydration heat increased sharply. If ambient temperature was too high, the pavement would face the risk of cracking because maximum stress exceeded flexural-tensile strength at any time. To reduce the risk of early cracking, the curing method without supplement water after covering should be avoided. Meanwhile, supplement water to cover in a timely manner and cover PE film at the same time was the priority curing method, if water was sufficient.

This study first evaluates statistical analysis as a credible method in finding significant reasons affecting the appearance of pavement distress. The urban network of Sohag city in Egypt is the case study where the pavement deformation is tested by statistical analysis against different independent variables. The analysis results are compared with the results of other experimental studies. The visual pavement distresses survey as the statistical analysis results agreed with the experimental results in explaining the appearance and propagation of pavement deformation. This comparison validates the statistical analysis to be used in pavement evaluation rather than conducting field and laboratory tests. Moreover, this agreement illustrates that statistical analysis can be used in generating accurate prediction models. Secondly, a prediction model for raveling distress is derived after investigating the affecting factors by statistical analysis. This model describes the propagation of raveling with time and traffic repetition in hot and dry climate zones.

This paper uses a three-dimensional (3D) discrete element method (DEM) to investigate the influence of aggregate gradation on the high temperature stability of structural-layer asphalt mixtures, evaluated by the uniaxial penetration test and creep test. Particle flow code in three dimensions was used to establish 3D models for the uniaxial penetration test and creep test. These models were used to simulate shear strength and creep behavior of structural-layer asphalt mixtures. Results were verified by performing actual uniaxial penetration tests and creep tests. The results showed that the high temperature stability of structural-layer asphalt mixtures could be simulated well by using the 3D DEM, and aggregate gradation significantly affected the high temperature stability of structural-layer asphalt mixtures. With an increase in coarse aggregate, particularly with a greater diameter, the shear strength increased, and the creep rate decreased, implying that the high temperature stability of structural-layer asphalt mixtures improved.

This study aimed to investigate the dynamic response of roller-compacted concrete (RCC)-base asphalt pavement under traffic loads. Several strain and temperature sensors were embedded in a test road at different depths. The dynamic responses driven by truck passage were tested, and the effects of axle load, vehicle speed, vehicle type, and temperature on the dynamic response were investigated. The results showed that the transverse strain in asphalt surfaces was tensile, while the longitudinal strain alternated in tension and compression. The longitudinal and transverse strains in the base and subbase were in tension and compression, respectively, and the longitudinal strain was greater than the transverse strain. The tensile strain in asphalt pavement increased with decreasing vehicle speed, increasing axle load. When the axle weight of the truck was doubled, the tensile strain in the upper and lower layers of the asphalt pavement increased under low temperature and low speed conditions.

Road roughness is a significant index to evaluate driving comfort and safety. However, the evaluation commonly used both in China and abroad shows great subjectivity due to the lack of information about passengers’ physiological responses. To make evaluation more objective and comprehensive, this research analyzes the change of various physiological indexes on different roughness conditions. The result indicates that road roughness has a definite impact on physiological indexes and there is a logarithmic relationship between the range of heart rate and the average of international roughness index (IRI), which shows that it is reasonable to use the combination of IRI and heart rate to reflect road comfort and safety.

The main objective of the paper is to find suitable constitutive models used for describing the linear viscoelasticity (LVE) of asphalt concrete by comparative analysis of three viscoelastic models, including traditional generalized Maxwell (GM) model, fractional generalized Maxwell (FGM) model, and 2S2P1D (2 Springs, 2 Parabolic, 1 Dashpot) model. A multi-objective nonlinear optimization model was established according to the residual sum of squares of storage modulus and loss modulus from dynamic modulus test. The results of statistical analysis indicated that the fitting curves of fractional models with fewer mechanical elements and fitting parameters were much smoother in comparison with the traditional integer model with many unknown parameters. Therefore, the fractional model is more suitable to characterize the viscoelastic mechanical behavior of asphalt concrete within a wide frequency and temperature range.

Asphalt mixture for railway substructure is subject to complex external conditions such as temperature and train cyclic load. Therefore, the excellent resistance to deformation, cracking, and fatigue failure of asphalt materials should be taken into consideration. In this research, based on the functional requirements of an asphalt railway substructure project in North China, a type of styrene-butadiene-styrene and crumb rubber composite modified asphalt (SBS+CR CMA) was proposed at first. Afterwards, several tests were performed to evaluate its thermal stability and fatigue resistance at the level of binder and mixture, respectively. Finally, compared with results of SBS+TPS (tough polystyrene) CMA and SBS MA, it was concluded that SBS+CR CMA showed much better low temperature cracking and fatigue resistance performances than the other two asphalts, although it had a weaker high temperature performance. Therefore, the proposed SBS+CR CMA was more suitable for this project and those applications in cold regions.

This paper proposes a new approach to quantify the lateral permeability and retaining water effect on the drainage capability of open-graded asphalt mixture. Based on the water balance principle, a new device was designed to measure the lateral permeability of the open-graded asphalt mixture. The lateral permeability of open-graded asphalt mixture samples with different air voids ratio and height were compared. The permeability coefficient of vertical, lateral, and retaining water volume was identified to evaluate water permeability of open-graded asphalt mixture. Results show that lateral permeability is affected by a variation of voids ratio and height. Obvious lateral penetration appeared with the decline of voids ratio, and more flow that is lateral would induce as the increment of height for open-graded asphalt mixture. The retaining water can indirectly reflect the connected voids inside. Compared to vertical permeability, improving the capacity of lateral permeability is the effective way to drainage.

This paper analyzes the fatigue failure and model degradation reliability of cement concrete for highway pavement. The fatigue test results were investigated and analyzed through the Weibull distribution. The distribution parameters were estimated by several numerical methods. The average parameters were used to incorporate probability distribution into the cumulative process of flexural fatigue damage. A new calculating method of the reliability was proposed. The reliability of concrete in pavement was calculated. The results show that the flexural fatigue life of cement concrete in pavements follows the two-parameter Weibull distribution. The Weibull parameters obtained are close to each other. The dispersion of flexural fatigue life decreased with the increase of applied stress level. The evolution of reliability for concrete in pavement consists of three stages and may correspond to different stages of cracking.

Asphalt generally behaves as an insulator. The addition of graphite can produce electrically conductive asphalt, which can be further manufactured as multifunctional asphalt concrete. The present study investigated the effect of graphite on the electrical conductivity and rheological characteristics of base asphalt. A novel electrical resistivity measuring method of asphalt binder was also introduced in detail. Various binder tests, including penetration, ductility, softening point, and dynamic shear rheometer (DSR) tests, were utilized to determine rheological properties of asphalt binder. Experimental results indicated that the conductivity of asphalt binder improved with the increasing content of graphite. Moreover, asphalt binder exhibited improved viscoelastic response and resistance to rutting due to the addition of graphite. However, the low-temperature performance of graphite modified asphalt was not encouraging from the ductility test. Therefore, asphalt binder containing graphite is promising to become novel road functional materials to meet both requirements of electrical conductivity and road properties.

This research sought to explain the rejuvenating mechanism of aged asphalt with bio-rejuvenator. Studies on rejuvenating mechanism of aged asphalt have mainly focused on the qualitative analysis in the functional group by Fourier transform infrared spectroscopy (FTIR) but not quantitatively on the amount of functional groups. This study aims to reveal the rejuvenating mechanism of bio-rejuvenated base and SBS modified asphalts by quantitative analysis. Results illustrated that the fractions in asphalt could be slightly balanced after restoration. Moreover, comparison between theoretical calculation and testing data indicated that chemical transmission occurred between four components. FTIR results indicated that new functional group wasn’t identified in base and SBS-modified binders’ rehabilitation process. However, analyzing A₁₇₃₅/A₈₁₁ with different dosages of bio-rejuvenator no longer showed a linear relationship. It also indicated that aged binder rehabilitation with bio-rejuvenator isn’t merely a physical mix but also involves a chemical reaction.

Surface texture is significant in skid resistance of asphalt pavement. This paper investigates the effect of surface texture variation on skid resistance of asphalt pavement during service. Texture depth (TD), British pendulum number (BPN), and dynamic friction coefficient (DF) of two different asphalt mixtures were tested by laboratory experiments under different polishing times. Fractal dimension was calculated according to the surface texture data of specimens obtained by 3D laser scanner. A numerical model of road surface texture and friction coefficient was established, and the correlation between calculated friction coefficient and friction coefficient measured in practice was analyzed. The results show that the fractal dimension and skid resistance of asphalt pavement both weakened with increased polishing time. It was also found that fractal dimension is in good correlation with skid resistance indicators. Moreover, the proposed numerical model can effectively predict road surface friction coefficient from macro- and micro-texture at any speed.

The objective of this paper is to study the relationship between four components and rejuvenated asphalt properties. Long-term aged asphalt was restored with varied dosages of bio-rejuvenator. Rejuvenated asphalt samples were separated into four fractions (aromatics, asphaltenes, resins, and saturates) based on component analysis. Binder properties, consisting of viscosity, performance grade, and fatigue resistance were measured. The chemical composition influence of each fraction on binder properties was analyzed qualitatively by grey correlation analysis. The results showed aromatics were important in affecting low temperature performance. Asphaltenes had a significant effect on viscosity and saturates greatly affected high temperature performance. It was found that asphaltenes exerted a negative influence on fatigue resistance. The results demonstrated the performance variations of binders during aging and rejuvenating process agreed well with the correlation relationship between performance properties and composition fractions. Grey correlation analysis was validated to indicate the performance variation in aged asphalts’ rehabilitation process.

In order to evaluate the high temperature performance of SBS modified asphalt, the essential influential factors of high temperature performance were investigated. First, five representative SBS modified asphalts were prepared. Second, the high temperature properties of the five modified asphalts were evaluated by softening point, dynamic viscosity, and multiple stress repeated creep recovery test index. The findings showed that the influence of SBS modifier content is the most influential factor. The non-recoverable creep compliance is very sensitive and can effectively characterize the high temperature performance of modified asphalt. The dynamic viscosity at 60°C was sensitive to the change of SBS modifier content, but insensitive to the other two factors. The softening point has a good correlation with the non-recoverable creep compliance and the recoverable strain. The changing of softening point before and after short-term aging can effectively reflect the difference in high temperature performance of modified asphalts.

In order to study the applicability of the vacuum decompression capillary method in testing the dynamic viscosity of SBS modified asphalt at 60°C, the effects of test equipment, test method, test environment, operator’s manipulation, and capillary calculation constant on the dynamic viscosity test results of SBS modified asphalt at 60°C were compared and analyzed. The test results show that, when all other factors were well controlled, the repeatability errors were 12.4% and 61.4%, which is far from satisfying the requirement (≤7%), and the reproducibility error was far from satisfying the requirement of no more than 10%. The coefficient of variation of the test results varied from 19.1% to 63.4%, indicating that the test results could not guarantee the accuracy of the test, and it is not suitable to be a test method for dynamic viscosity of SBS modified asphalt at 60°C.

To study the effect of SBS content on the elastic-plastic properties and durability of asphalt, the change in the spatial interaction network of SBS modified asphalt was explored. Modified asphalt with SBS dosages of 2.5%, 3.0%, and 4.0% were prepared and the elastic-plastic properties were tested. The changes were investigated through the FTIR test. The findings showed that by changing SBS content, the softening point, penetration index, ductility, and viscosity-toughness did not exhibit an obvious trend, indicating these indexes are not sensitive to SBS content. After the short-term aging, all the indexes showed a continuous trend, indicating that asphalt after short-term aging is more suitable to evaluate performance variance. The addition of SBS introduced new functional groups into the matrix. However, the increase of SBS content can only be seen in functional groups, but does not change the spatial network.

In order to detect the content of SBS modifier in SBS modified asphalt with high precision, the principle of image analysis based on near infrared spectroscopy was deeply analyzed, and the infrared spectrum analysis method of three-dimensional analytical method was proposed. The results show that: (1) the peak position of absorbance at wave number 967 cm⁻¹ is the characteristic peak position of SBS modifier different from other substances in modified asphalt; (2) the content of SBS modifier can be detected by calculating the absorbance peak area at 967 cm⁻¹ wave number position; and (3) compared with the traditional two-dimensional analytical method of infrared spectrogram, the proposed three-dimensional analytical method of infrared spectrogram with sub-band has only 0.09% deviation from the true value, and the detection accuracy has been improved by 15%.

To study the development law of transverse cracks, the multi-years crack detection data in Jiangsu provincial expressway were analyzed, and the crack number and length were observed over a long-term for a typical section. Considering influence factors such as environment and pavement materials, the development law of transverse crack density with road age was analyzed, and the model was established to carry out prediction analysis using the structural behavioral equation. The results show that the generation and expansion of transverse cracks are greatly affected by low temperature. Compared with AK-13 or PAC-13, the crack density is smaller when SMA-13 is used as the surface layer material. The development law of transverse cracks presents three-stage characteristics of initial development, steady development, and rapid development, which can be characterized by structural behavior equations. When the pavement is maintained over time, the cracks develop slowly, and the equation parameter of β is small.

In order to meet the traffic requirements of road subgrade in soft soil area, the method of composite reinforcement of geosynthetic materials based on soft soil subgrade is proposed. Firstly, the morphological characteristics and working principle of the reinforcement method are introduced. The soil quality analysis is then carried out based on the engineering survey. Soil parameters such as moisture content limit, compressibility, and permeability are determined. In the end, the road settlement calculation is carried out based on the composite reinforcement method of geosynthetics. The results show that the method is effective according to the JKR ground settlement criteria.

In order to explore the effect of base temperature on early cracking of concrete pavement slabs, the concrete surface was paved on six kinds of bases whose temperature was 10, 15, 20, 25, 30, and 35°C, and the stress strength ratio of concrete slabs within 72 h after paving were calculated and the risks of cracking were also compared. The results show that: The higher the base temperature, the earlier the structural stress occurs. In order to reduce the risk of slab cracking, the concrete surface should be paved when the base temperature is low; within 48 h after concrete pouring, the maximum principal stress growth rate caused by the high base temperature is much greater than the tensile strength. During this period, the concrete strength is still low, the stress develops rapidly, and the pavement structure is exposed to the risk of cracking at any time. It is necessary to pay attention to the external health curing during this period to reduce the high cracking risk faced by the slab; at the final setting time, the base temperature is the main reason for the difference of the stress strength ratio. After that, the ambient temperature gradually replaces the base temperature and becomes the dominant factor. Therefore, for the crack caused by the base temperature, the control measures should be applied before the final setting time, and the countermeasures for the cracking of the slab after the final set should be mainly designed for the superposition effect of the ambient temperature and the hydration heat.

The objective of this research is to explore the influence of high reclaimed asphalt pavement (RAP) content on the construction temperature. Five mixtures with asphalt contents of 2.8%, 3.3%, 3.8%, 4.3%, and 4.8% and RAP content of 20%, 35%, and 50% were analyzed. The optimal asphalt content (OAC) increases with the increasing RAP content, while the added asphalt content decreases. The discharge temperature has a negative correlation with the RAP content. The higher RAP contents and initial water content result in the decrease of paving temperature. Simulated temperature segregation tests show that lower RAP content has little influence on the temperature segregation. Results in the study indicate that RAP could be successfully used in recycled asphalt mixtures depending on the OAC, thermal insulation measures, and suitable climatic conditions.

During the service lives of double-layer porous asphalt pavements, drainage performance, and the noise-reduction capacity are noticeably weakened by dust and polluted rainwater. This paper aims to increase self-cleaning performance and reduce maintenance frequencies to cut down the expenses of dust-cleaning actions. Fluid-structure coupled simulations using Flow-3D software were used to simulate self-cleaning performance of double-layer porous asphalt pavements with different granular diameters (4, 8, and 16 mm) and layer combinations (3.5 cm + 6.5 cm, 4 cm + 6 cm, and 4.5 cm + 5.5 cm). The results showed that equivalent granular diameters significantly influenced water pressure inside interconnected pores and 8-mm granular diameter was preferred to provide good self-cleaning performance. However, layer combination exhibited negligible impact on the self-cleaning performance of double-layer porous asphalt pavement.

The bus rapid transit (BRT) system is a large capacity and fast public transportation operation mode. The BRT station is the bottleneck of the whole system, which determines the operation capability of BRT. The time benefit of passengers is the time when the passenger leaves the bus rapid transit vehicle to leave the motor vehicle lane. This paper establishes a delay model based on the time benefit of passengers, and uses the VISSIM software to simulate the delay model. The station settings at an intersection of Jinan BRT1 line (median stop) are used as an example. Model parameters setting are according to the actual data of the survey line. Based on the time benefit of passengers, both calculation and simulation results shows that the station is better at the exit of the intersection than the entrance.

In recent years, public transportation has occupied the leading status among other modes of transport. The prediction of public transit passenger volume is directly related to the scientific basis and rationality of urban transit planning. In this paper, historical data and influencing factors of public transit passenger volume of Beijing are fully considered from five aspects (bus system, competitors, travel demand, urban construction development, and incidental events). After the implement of gray correlation degree, nine influencing indicators of greater correlation with public transit passenger volume are used as comparison sequences for subsequent prediction. Then based on the normalized data, this paper establishes a gray neural network to predict public transit passenger volume by MATLAB programming. Finally, the prediction accuracy of the gray neural network model is proved by comparing with the results of a single BP neural network model.

This paper describes a method of bi-modal public transport network optimization considering the relationships between bus rapid transit (BRT) routes and conventional bus routes. BRT has large capacity and speed. In order to reduce travel time in the bus network, bus passengers should transfer to BRT often. The paper establishes the optimization model of public transportation network combining the competitive and cooperative relationship concepts of the bus network and ensure that public transport can cover all the original bus sites. Lanzhou, a medium-sized city in China, has two kinds of public transport modes, conventional bus and BRT. After BRT routes were added, not enough attention was paid to cooperation and competition between BRT and conventional routes. This paper shows how to optimize the network of Lanzhou. Comparing the optimization network with the current network, the average passengers travel time in network is significantly lower than the current one.

The variability of traffic flows and passenger arrival patterns lead to unstable bus operation systems. To improve the level of bus service and maintain stable headways, a bus speed guidance model is proposed in this paper. Based on the time that buses depart from or arrive at bus stops, the model determines the holding time at bus stop and the recommended travel speed between stops. Meanwhile, combined with queuing information at intersections, the recommended speed at station-to-intersection section is calculated to reduce the average delay at intersections. Finally, different headway variances and travel time fluctuations are set to validate the proposed control model. The results show that under the speed guidance model, the variance coefficient of headways and the average passenger waiting time can be reduced apparently.

To improve the public traffic’s utilization efficiency and make residents’ travel more convenient, this paper sets up a public transit network optimization model from the aspect of shortening the passengers’ waiting time and maximizing the benefits of public traffic company. It divides each traffic zone on the basis of time and space properties, then gets the traffic OD of each base station community by processing and analyzing the mobile phones’ data according to the situation of Suqian. A four-step method was used to make a prediction of the requirement for public traffic volume. A genetic algorithm was adapted to figure out the public traffic network’s optimization. The results show that a small part of bus routes need to change. In addition, compared with the existing public traffic network, the optimized scheme increases the network’s density, and decreases line nonlinear and overlap factor, which meets the travel needs in Suqian better.

Travel time of buses from start station to end station is a key indicator of operational performance. It is of significance for arranging and dispatching vehicles by operators. In this paper, we analyze the travel time of buses with GPS (global positioning system) trajectory data. The results show that there exist obvious peaks during rush hours on weekdays, which means the travel time is larger in rush hours than other hours. Moreover, the distribution of travel time follows a right skewed distribution. Additionally, we divide the travel time into running time of sections and dwell time at stations and analyze their performance specifically. Finally, a BP neural network prediction model is introduced to predict travel time of buses using the historic travel time with time intervals. The results show that it outperforms Gauss fit and Kalman filter methods.

The Metro is a significant part of a transit system as it provides service to millions of people every day. During peak hours, rail transit hub stations are packed with commuters; therefore it is extremely important to evacuate all occupants safely and rapidly when a catastrophe occurs. In this study, we focused on a typical urban rail transit hub in Shanghai: the Caobao Road Metro Station. Spatial data and population parameters are collected to set up a geometric model. We conducted dozens of evacuation simulations for different scenarios and established a ranking standard to assess the level of service of a station. The analysis combines evacuation time, pedestrian density, exit efficiency, and potential risks. The results show that the hub has an imbalance on efficiency of exits. This research can help understand the potential dangers and provide scientific suggestions for metro administration and supervision authorities.

Crowd theory has drawn much attention from researchers owing to the sustained growth of urban population and transit passengers. Researchers have focused on modeling crowds and recording trajectories in order to analyze the characteristics of pedestrian flow and quantify person–person and person–environment interactions. This paper presents a survey of research on pedestrian flow. We depict the history of this research from the 1970s to the present and explore the points that researchers focused on. We then classify the research objectives and methods and summarize the relative achievements. Some advantages and disadvantages of different methods are discussed to help future researchers select suitable methods. Our review ends by indicating some possible future trends and challenges and proposing some possible directions for future research.

This study focuses on the construction of an agent-based simulation system for a round-trip bus line. Generalized system cost is used as the evaluation index in this system. First, the agent-based model framework and process of bus transit line simulation is established. Second, an agent-based simulation system of bus transit line is developed. This system includes three main function modules which combine with the updating of system operation parameters and the cost statistics data to complete simulation of the dynamic operation process of a bus transit line. Third, by using this system, several simulation experiments of different given scenarios (by changing the departure interval and stop spacing) are conducted to obtain the optimal operation plans, which provides an effective way to analyze the corresponding changes in bus operation system, evaluate its performance, and optimize the bus transit line.

Motorized travel behavior of the elderly is a result of the desired travel being affected by transit convenience. In this paper, the leisure travel of the elderly in Kunming is taken as an example. According to the “push-pull” theory, the behavior relevance model of leisure activity-travel of the elderly is established by the structural equation model method. The influence mechanism of transit convenience is analyzed, which is based on 74 qualitative interview data and 633 valid questionnaire sample data. Studies have shown that transit convenience has a significant influence on the choice of leisure locations of the elderly. The choice of leisure locations has the greatest influence on travel behavior. In the planning and design of urban leisure space, attention should be given to the functional requirements of the elderly and a reasonable layout. The improvement of transit convenience can reduce societal exclusion of the elderly population caused by transportation.

Due to its large capacity, safety and punctuality, urban rail transit has become one of the most important measures to promote public transport and relieve traffic congestion. The planning of rail transit service exerts considerable influence on conventional public transport and land use. In this paper, a comprehensive suitability evaluation system of urban rail transit system is presented. Three aspects are taken into consideration in this system: the coordination between urban rail transit systems and conventional bus systems, the coordination between urban rail transit systems and urban land use, and passenger transfer capacity analysis. Finally, an overall suitability index is presented to give a comprehensive evaluation on the suitability of urban rail transit system. The feasibility of this evaluation system is verified by the case study in Wuxi, China.

Based on the smart card and GPS records of No. 976 buses from April 13 to April 19, 2015 in Shanghai, this study conducted analyses on travel time and locations. It found that workday and weekends have a great influence on the travel time and location rules. On workdays, the commuting demand was more average at each station, with obvious peak hours in the morning and evening. During non-working hours, residents traveled into areas with more leisure and entertainment places. Furthermore, through clustering for card users, four types of trip characteristics were defined: random travelers, leisure travelers, commuters, and night actives. Different types have their own features on travel time, frequency, and station preferences. Using these situations, the significance of improving the operation of urban public transport system was explored.

The customized city bus is a mode of bus operation between conventional buses and taxis and is an important product of the diversified development of public transportation. This paper firstly builds the basic database by correlating and integrating its initial operation data based on the customized city bus in Xi’an, and the time and space distribution rules of passengers and the ticket booking behavior are analyzed. Then the seasonal index expansion method is used to fit the change rule of the number of phone application (APP) active users in 1 week and perform short-term forecasting by using the mobile phone terminal recorded data. Finally, the one-way running direction and the number of passengers transported in 1 day of the single bus are judged by the scan time of passengers based on K-means clustering. Through the above findings, a basis for the optimization of customized city bus operation management is provided.

With the increase of passenger flow pressures, metropolises reduce traffic pressure by adding new lines. To ensure the rationality of new lines, it is necessary to analyze the impact of different line types on the vulnerability of the existing rail transit network (RTN). Firstly, the new lines are divided into several types. Then, the models of different line types connected to the rail transit network are established by the L-space method. Moreover, a new method that comprehensively evaluates the rail transit network vulnerability based on the index of average shortest path length, network efficiency, and average node betweenness is proposed. Finally, taking Beijing as an example, obtain the influence characteristics on the vulnerability of Beijing rail transit network (BRTN) connected with different line types. The results show that the ring line can effectively reduce the vulnerability of the network compared with a penetrating line and outskirts extension line.

A public transportation system holds the promise of more efficient movement and fewer environmental problems. Rising its ridership attraction is an important goal of transportation demand management measures. Many cities around the world have implemented a free public transport policy, such as Tallinn and Melbourne, although their results are not ideal enough due to the inadequate planning. Thus, this paper further considers a peak hour fare-free zone in order to improve the urban mobility and social equity. Moreover, fare-free public transport refers to public transport funded in full by means other than by collecting fares from passengers, which involves the issue of resource reassignment. Hence, the social equity issue is considered as an evaluation criterion in this paper. In conclusion, this paper focuses on the feasibility as well as the public equity issue of fare-free public transport, trying to identify the optimal time period for implementing this policy based on social equity. Findings will be valuable for policy makers to evaluate fare-free public transport.

IC card data is an important data source of public transport passenger flow. From the perspective of data characteristics, this paper used the Qingdao bus No. 1 IC card data from January to May in 2018 to study the similarity and concentration degree of passenger flow based on the multi-time granularity. Besides, the different features between the peak and off-peak hours, as well as between the workdays and weekends with multi-time granularity were analysed. As for the passenger flow similarity, the statistical result shows that the data similarity increases gradually with the increasing of time granularities both on workdays and weekends, the range of similarity of passenger flow during peak hours is significantly more concentrated than off-peak hours, and the similarity degree of the passenger flow on workdays is slightly higher than that on weekends. As for the degree of concentration, it is positively correlated with the time granularity, and during peak hours, it is significantly higher than the off-peak hours. The mean relative error (RME) was then calculated by the prediction result based on the quadratic exponential smoothing method (QESM), which proved the speculation of the above conclusion.

Facing the demand of energy structure transformation and environmental pollution control in the transportation industry, the development of new energy buses have become an important measure for all countries to improve the urban traffic environment. There are great differences between China and Germany in the development of new energy buses for different development goals. Through the comparative study on development of objectives formulation, promotion strategy change, and implementation, we analyze the similarities and differences of new energy bus development path between China and Germany, and summarize their experience in order to provide effective recommendations for promoting new energy buses.

In order to improve the safety and evacuation efficiency at subway platforms, this paper discusses the applicability of staircase position to evacuation efficiency. First, we summarize the characteristics of passengers at the subway platform. After that, three different position of staircase exits are chosen to be the object of study and a simulation scenario by AnyLogic is built based on the data of a subway platform in Beijing. Finally, we analyze the applicability of three scenarios by the indictor of the number of passengers and time of evacuation process. The simulation results show that, in comparison with three forms of staircase, the staircase exits at the center of the platform have the worst evacuation capacity among the three options. In addition, other two scenario are fit to different volume conditions. Those results can provide some guidance to the facilities design in subway platform.

In order to propose a simulation method for the driving cycle of a purely electric bus, the research takes an operation line in Xi’an as example. It collects the real-time speed spectrum and operation data using VBOX3i, proposes to treat a single process from starting to stopping in inter-stations as a motion segment, and uses it to match the actual driving cycle and simulate the entire round trip. The simulation parameters are extracted. The test field is determined and parameters such as power consumption were tested. Finally, the test results are compared with the operation data, which were corrected in consideration of the driving skills and the number of passengers. The result shows the deviation of the energy-saving skills can be up to 3%, and the deviation of the power consumption is less than 3%, which verifies the feasibility and stability of the simulation method.

When multiple buses stop at the multi-berth bus stops at the same time, it is often the case that passengers cannot quickly determine the stop position of the pre-ride bus and run on the bus stop, which causes confusion. To this end, according to the bus positioning information, a real-time berth allocation method can be adopted. The pre-announcement function of the bus-to-station information and the real-time information update is carried out by the bus electronic station sign, and the bus and berth of the upcoming bus is intelligently matched =. The passengers can know the function of the bus stop position before the bus arrives at the station, shorten the passengers’ boarding time, reduce the bus parking delay, improve the bus operation efficiency, and reduce the safety problems caused by the passengers in the station waiting.

Accurate short-term forecast of real-time metro passenger flow is essential for improving the operation and management level of a metro system. This research establishes the linear model ARIMA (autoregressive integrated moving average) and nonlinear model NARNN (nonlinear autoregression neural network) respectively to forecast the short-term entrance passenger flow of a metro station. The application experiment is based on AFC data of Confucius Temple station of Nanjing metro line three. Prediction time interval is set as 5 and 15 min. Comparison results show that accuracy of both models is higher in 15 min interval than in 5 min interval. NARNN has greater accuracy and adaptability than the ARMIA model. However, there is slight lag in the forecast of the NARNN model, while the prediction errors of the ARIMA model increase with time. The results indicate a requirement for optimal or combined methods to improve the accuracy of shorter time interval forecasting.

Since the revival of streetcars in the 1990s, many cities in China have actively planned and built modern streetcars, but their operation conditions are varied. Although domestic scholars have carried out a series of research on its development process, application modes, and benefit evaluation, it is necessary to provide a method to make an objective appraisal on modern streetcars in China. This paper analyzes the functions of modern streetcars in different types of cities and regions. On the foundation of defining its function orientation, a post-evaluation framework is established. By this framework, the adaptability of modern streetcars can be evaluated scientifically and rationally, then providing reference for the decision-making of developing modern streetcars in the future.

Under the free development environment, public bicycles (PB) and free-floating bike sharing (FFBS) often overlap for service. This study explores the feasibility of spatial cooperation of the two systems in Nanjing based on multivariate data. The FFBS trips are divided into similar and supplementary trips for PB, and used to compare with PB for spatial usage characteristics. And the surrounding feature differences of PB and FFBS dominant stations are analyzed through 13 factors with GIS. The results indicate the dissimilarity between the two systems. And this study proposes to place major FFBS in the area with dense roads, large demand sources, and scattered location, especially the area with more entertainment venues. The findings provide a scientific basis for the measures for spatial cooperation, which is of great significance to the sustainable development of urban bike-sharing systems.

With the rapid development of economy and society, congestion in cities is increasing and headway for public transport is unstable. Therefore, bus bunching is occurring in the course of bus operation, which leads to unreliable service problems such as increased overall waiting time. Under the premise of the widely used smart card, the research takes a single bus line as the object and studies it with the historical data of the smart card. A real-time model of passengers waiting at bus stops is established based on the data of bus speed and historical data. Moreover, the prediction result is used to predict the bus bunching. This paper will verify the efficiency and feasibility of the model by numerical examples. The study’s results show that the prediction model of bus bunching based on smart card data can predict the condition during bus operation accurately and discover potential problems in time.

Urbanization has brought about economic growth and the deterioration of traffic environment. Rail transit is becoming a primary mode of urban public passenger transport because of its many advantages. However, rail transit is only built for the key passenger flow routes in the city. Only by effectively connecting with other traffic modes can it maximize the vitality and advantages of rail transit, expand the scope of passenger flow attraction, form a more reasonable urban traffic system, and improve urban traffic problems. This paper establishes an evaluation system of rail transit connection modes combined with the three modes of transportation: bus, car, and bicycle. It evaluates and analyzes the North Street Station of Xi’an Metro Line 2. It concludes with the main factors restricting the connection level of rail transit, hoping to provide some reference for the future planning and construction of rail transit.

The main object of this study is Hellobike, whose management has not yet been systematized in Jingjiang. The mathematical expectation of the distance from starting place to a useful Hellobike can be found by Matlab, in the case of random distribution, with a certain possibility that some Hellobikes are unusable. The mathematical expectation is then compared with that of traditional public bicycle, which has a fixed pile position. It is referred in this study that Hellobike will lose the advantage that it can be borrowed everywhere if the non-use rate reaches 0.9, which is basically in sync with current circumstance based on real sampling data. Therefore, the management of Hellobike in Jingjiang is critical.

The scientific classification of metro-bus hubs can obtain the commonality of traffic characteristics in various categories. Based on the POI and smart card (SC) data, this paper captures the hub’s attribute and surrounding environment variables by multi-source data fusion technology. Principal component analysis is made to extract factors from variables. Using K-means cluster analysis, 113 metro-bus intermodal hubs in Nanjing are classified into five categories. In addition, this paper analyzes the demand characteristics of hubs. The classification results show three categories of hubs have a great influence on the traffic network, which are the city-center, metro-bus oriented, and traffic-expansion hubs. The main reasons for the delay in the hub are analyzed, and the optimization for the transfer design on different categories of hubs is provided.

The market share of public transport continues to decline as more and more flexible modes of travel appear. A public transportation service quality evaluation system advancing with the times is conducive to improving the attractiveness of public transportation. This paper presents the composition and characteristics of MaaS (mobility as a service), based on a study of MaaS development. With the goal of improving bus transit service quality, a bus service level evaluation system is established. The system will identify new criteria and indexes, apply Delphi and AHP to obtain weights of indexes, and establish a model of evaluation. Finally, this paper concludes with the advantages of the bus service quality evaluation system and future prospects.

Trams in urban transport have seen rapid growth in recent years, yet poor coordination between it and other vehicles hinders the full achievement of its potential in the transport system. To cope with the current lack of study on green wave with respect to trams, a progression model is put forward in this paper based on the MAXBAND model and the multi-path progression model. The proposed model mainly addresses the specific scenario in the arterial green-wave design with tram: one tram travels along the artery in one direction while another in the opposite direction turns right at one of the arterial intersections. In this model, the goal of optimization is the bandwidth of general vehicles under the constraint of some expected bandwidth for tram green wave. Phase sequence at each intersection serves as a decision variable. The calculation of this model shows that it proffers satisfactory results and offers a feasible solution.

As a safe and convenient transportation service, the school bus currently plays an important role in students’ commute in China. A variant called school bus stop selection and routing problem with walking accessibility (SBSSRPWA) that considers the individual differences among students is introduced in this paper. A mixed integer programming model is proposed for SBSSRPWA aiming at minimizing the total traveling time from home to school of all students. A two-stage solution method is proposed incorporating a hybrid density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering stop selection method that considers various walking accessibilities as stop coverages in stage 1. An improved ant colony optimization routing algorithm based on problem characteristics is introduced in stage 2. The proposed solution method is conducted on a randomly generated instance.

Passengers’ travel experience on previous days will potentially influence their transit route choices on the next day. The day-to-day information is an important reference for decision-making of the transit assignment. This paper investigates the frequency-based transit assignment problem in a learning-based, day-to-day dynamics environment taking the learning and prediction behavior into consideration. The passengers’ route cost updating, as well as the flow updating, are explicitly considered and incorporated in the day-to-day learning process of the transit assignment model. With the proposed cost updating and flow updating process, the frequency-based optimization model of the day-to-day transit assignment problem is formulated. Afterwards, the model is solved with the method of successive averages. Finally, the numerical experiment validates the effectiveness of the proposed model and algorithm. It is revealed that the passengers’ flow evolution process will tend to be stable due to the day-to-day learning mechanism.

Public transport is the main travel mode for people to participate in social activities. The definition of transit equity is defined from the perspective of the traveler and generalized utility theory is used to analyze the factors influencing transit equity. The objective function combines the travel characteristics of public transport and socioeconomic characteristics of travelers together to find their influences on transit equity. The result shows that the equity of transit services is proven to be heavily influenced by traveler’s satisfaction to the comfort and punctuality rate of transit. Transferring times, walking time, and waiting time for certain bus routes do not have a great effect on equity. The study presented in this paper facilitates the diversified analysis of the public transport equity and provides a framework for planners and policy-makers to measure equity objectively.

The flex-route transit service (FTS) is one of the most common types of flexible transit services, which generally drives along a base route in general and also provides door-to-door service for passengers by deviation. In order to make FTS more reasonable and practicable, considering the relationship of passenger satisfaction and tolerable service time windows, this paper builds a multi-objective optimal model to optimize vehicle routing of FTS. The objective is to minimize vehicle travel time as well as maximizing passenger satisfaction. By considering passenger service time, passenger numbers, and location of dynamic stations between two checkpoints, design an insertion heuristic, and generate optimal vehicle routing. Finally, simulation experiments are used to validate the presented model. The results of this paper show that our approach ensures vehicles travel along an optimal route, reducing operational delay, and improving service quality.

This paper proposes a method for assessing supply level of public transit from a demand-oriented perspective. Online taxi-hailing order is chosen for travel demand analysis because it reflects the potential travel needs of public transit and provides a large sample with precision location information. Besides, a series of variables integrated with travel demand are employed to describe public transit supply: public transit service availability, public transit station coverage mass, transit line coverage mass, and public transit connectivity. Geographical links between travel demand and these variables are established with a local regression model—geographically weighted regression (GWR). In the model, the calibrated local coefficients of different variables are endowed with function of supply assessment. Applying the method in Chengdu as a case study, results demonstrate the variation of public transit attraction against online taxi-hailing services in each section and indicate public transit supply gaps in different areas.

Public participation has become an important part of traffic planning and policy-making, but due to the limits of project cycle and cost, collecting and processing public opinions on a large scale seems to be a huge challenge. Relevant government departments should accept public opinions and make scientific and reasonable policies. This study focuses on how to use big data from social network platforms for content analysis. Data about public opinions are collected from the internet by means of a web crawler. A latent Dirichlet allocation (LDA) topic model is built to summarize public opinions and traffic problems. This paper also uses the Nanjing subway system as an example to introduce the whole procedure of content analysis and sum up the spatiotemporal properties of data. According to the results, some corresponding measures for the Nanjing metro system are proposed to improve the operation and management.

This paper proposes a variable-bandwidth approach to the arterial traffic signal optimization with priority of straight and left-turn trams based on MULTIBAND. First, the basic constraints for green waves of the trams and the general traffic are analyzed. The vital work is the establishment of constraints for the critical parameters at intersections when trams go straight and turn left. Second, according to difference of the traffic flow on each directional road section, the weight coefficients of bandwidth on each link of the arterial can be defined to general traffic bandwidth for general traffic. The maximum and minimum values of some decision variables are established to meet the demands of tram systems and general traffic. Then, mixed-integer nonlinear programming is employed for the optimization. Finally, a case study is presented to indicate that this approach can meet the demands of trams, while improving general traffic significantly.

Bus bunching seriously damages the quality of transit service. This paper proposes a predictive approach to detect the occurrence of bus bunching depending on transit smart card data analysis. Several different support vector machine (SVM) algorithms are established to detect bus bunching based on the stop-level headway irregularity. An empirical experiment in Changzhou is conducted to analyze and compare the performances of different algorithms in practical applications. By comparing the prediction accuracy and computation efficiency, the optimal model is determined under different scenarios. The results suggest transit operators should use least squares support vector machine with linear kernel when the size of training dataset is large. Traditional SVM with linear kernel might be a better option for ensuring the prediction accuracy on smaller datasets. This strategy may improve transit service and increase transit ridership.

Describing the traffic characteristics of pedestrians and analyzing station carrying capacity is important for station design planning and operation management. Therefore this paper analyzes the characteristics of pedestrian traffic and behavior and proposes the concepts of station basic carrying capacity and station inbound capacity. The definition and calculation method of station basic carrying capacity and station inbound capacity is then proposed. There is a large gap between the solution of the model by using analytical methods and actual analysis, so this paper proposes the simulation algorithm based on a “stress test” by using Anylogic software. Station bottlenecks are found to provide suggestions for station pedestrian flow control. Finally, this paper uses the Xiaozhai station as an example to study and analyze station carrying capacity; results show the basic and inbound carrying capacity is 6,042 person and 37,000 per/h, verifying the validity and practicability of the model algorithm.

Rail transit stations play an important role in the entire railway system. This paper took the PROMETHEE (preference ranking organization method for enrich of evaluation) method to perform comprehensive evaluation on railway station planning scenarios. Firstly, based on station characteristics, the evaluation indicators were selected and quantified from network structure, feeder traffic facilities, and land development, which are node degrees, betweenness, closeness, equivalent road network density, feeder facility completion level, commercial land efficiency, and residential land efficiency. Secondly, the method of comprehensive evaluation based on PROMETHEE was proposed. Finally, the R2 line-planning scenario of Jinan was taken as an example. The evaluation results show that the stations are obviously classified according to land use. The comprehensive evaluation of regional center stations ranked higher than community center stations. From the geometrical analysis for interactive aid (GAIA) plane, we identified stations that with good locations but are not well developed. In addition, we put forward and verified the advice that equivalent road network density should be paid more attention in this scenario.

As Chinese cities continue to expand, the number of people moving to the suburbs has trended upwards. However, many of them work in downtown, and most suburban residents prefer driving as their daily commute mode due to patchy transportation. This paper studies whether the travel mode of residents changed after the operation of the subway in the suburbs by using 2 years bike-sharing usage data in Wujiang District. It explores the question by comparing the heatmaps of 2 years’ bike-sharing usage drawn by ArcGIS and analyzing the socio-demographics associated with Bike+ Rail (B + R) tendency through an ordinal regression. The result shows that there was a sharp shift in bike-sharing usage since the advent of subway, and B + R was chosen as the new commuting mode by more residents. The attributes that facilitating B + R mode use are increasing age, being male, and longer commute distances.

In this paper, we proposed a scheduling method of sharing automated taxi service based on multi-objective decision, which defines that the system provides transportation services in urban areas and achieves effective ways of door-to-door transportation service. Considering the user’s willingness to carpool and the cost of travel time and route, we established a model and proposed an improved PSO algorithm for model solving. In this way, we can realize sharing travel. Ultimately, we conducted a small case study, using Matlab to carry out simulation experiments, applying the automated taxi system to a business district. In order to achieve system optimization based on known passengers’ travel demand such as origin, destination, and travel time, we prefer users with the same travel route when the automated taxi matches. The results show that the model is suitable for small case studies, and the algorithm has good convergence and stability.

With the development of a new generation of information technology, bicycle sharing as the preferred way to solve urban “last mile” travel has been favored by people. However, there are also many problems in its operation. The most common problems are “no shared bicycles to borrow” and “no land to return the shared bicycles.” The reason for this is the dispatching and management of the shared bicycles. This paper studies the dispatching and management of no pile bicycle sharing, and constructs a dispatching model based on semi-open multi-vehicle with fuzzy time window with the number of distribution vehicles and dispatching cost as the objective function, and designs a Tabu search algorithm for it. The solution is to solve the problem that some of the placement points share; the bicycle accumulation or the shared bicycles can be borrowed.

The unobstructed situation of urban passenger transit corridors directly affects the efficiency of urban traffic operations. This paper analyzes the characteristics of bus operation, defines the bottleneck of the bus intersection and analyzes various factors related to it. Based on the bus big data, the identification index, and calculation method of the bottleneck of the bus intersection are proposed, and the bottleneck identification system is constructed. Finally, taking the passenger transit corridor of Guanhe Road in Changzhou City, Jiangsu Province as an example, the time, space, and severity of the bus bottleneck running on the Guanhe Road Corridor were identified, and the typical causes were analyzed. It is of great practical significance to promote the construction of the bottleneck identification system for bus intersections based on public transport big data, which will help to improve the operational efficiency of the ground crossings in passenger transit corridors.

This paper establishes the average queue length model of bus stops based on Markov chain process to determine the length of bus stop zones. Furthermore, this paper utilizes a VISSIM model to study a typical bus stop simulation and certifies the Markov model in order to determine the feasibility and reasonableness of zone length. This paper optimizes the average queue length, maximum queue length, and delays in a typical bus stop. Based on the statistical results, the paper determines reasonable reconstruction programs and gives optimization design instances.

Bicycle transportation is a green and environmentally-friendly travel mode with unique advantages in the development of urban transportation and plays an important role in alleviating urban traffic congestion and reducing environmental pollution, especially in tourist cities. Based on relevant literature and the analysis of the current status of domestic bicycle traffic development, this paper proposes a quadratic exponential smoothing method for demand forecasting, a P-middle model for site planning, and a fuzzy comprehensive evaluation method. The method for planning and designing the public bicycle network in an urban scenic spot was conducted using the Datang Furong Garden as site.

Passenger flow forecasting is the basis of the rail transit operation department. In the context of the big data era, this paper proposes a new short-term incoming passenger flow forecasting method for rail transit. In this paper, an improved neural network model is trained on the spark distributed parallel computing framework to reduce training time, and a new method to divide data types is proposed. In order to select the input of the model, this paper implements a correlation measure method to characterize the relationship between the influencing factors and the predicted target. Finally, the proposed model is validated by the actual passenger flow data of Guangzhou Metro. The results show that the prediction accuracy of passenger flow is ideal and meets the project requirements.

In order to promote the coordinated development of subways and public transportation in emerging metro cities, optimization of the urban public transportation network is needed. Based on utility theory, this paperanalyzes the elastic characteristics of bus demand to distribute the line traffic under the elastic demand. By maximizing the bus connection system passenger flow and mimimizingpassenger travel cost and vehicle equipment cost as the optimization goal, we established the two-level planning model for the ground bus line adjustment. The MSA algorithm is used to solve the lower layer model, and the upper model is solved by PSO-X algorithm to obtain the optimal connection bus network. Finally, through simulation experiments we obtained the optimization scheme of the connected bus network of a subway station, which verifies the rationality and usability of the model and algorithm.

The concept of accessibility has been widely adopted in the transportation planning field, but available research mainly focuses on using accessibility as an indicator for evaluating the overall transportation system performance. Limited studies on implementing accessibility in the decision-making process of transportation planning have been found in the literature, due to the shortage of appropriate mathematical methods for addressing accessibility and its measurement. Using Southampton as a case study, this research develops a practical accessibility model to evaluate the network performance for real-world size public transport networks, and to output more realistic results based on the available public-source input data. The methodology described has been shown to work well for the Southampton case study. While the methodology described here focuses on the UK context it potentially could be applied equally well in other contexts around the world.

This paper studies the storage strategy of a reverse logistics repurchase contract based on the newsboy model in an e-commerce platform. Based on the existing research, in order to expand the market share of new products and increase the expected revenue of the platform, this e-commerce platform implements a new ordering strategy by checking the inventory error rate. Based on the newsboy model, this paper establishes a storage strategy model of a reverse logistics repurchase contract. After analyzing the stock shrinkage rate the influence of the stock shrinkage rate on the optimal order quantity and profit is obtained. This is of great help to the purchase strategy of e-commerce platforms. Numerical examples are given to illustrate the results of the model. The results show that the inventory error rate has an important impact on the order quantity and profit. The e-commerce platform needs to pay more attention to this.

Passenger flow forecasting is important for transit service planning and operational management. Many models and techniques have been developed to address this issue. In order to improve the accuracy of short-term passenger flow forecasting, an enhanced K-nearest neighbor method is proposed in this paper. The method considers the trend factor and time interval factor of passenger flow in the stage of state vector design and distance measurement, and then avoids the risks of fewness of the evaluation criterion of the original method in the matching process. Based on smart card data from the automatic fare collection system of the subway in Beijing, we designed an experiment to test the ability of the new method and three models. The test results show that the improvement scheme has better performance of forecasting comparing with BP neural network model (BPNN), SARIMA model and the original KNN method.

Cluster analysis is a good tool to classify urban rail transit stations and figure out the difference between stations. By using clustering analysis, the intention of this paper is to find the difference between different kinds of metro stations. The method we used in this study is known as K-medoids, the input of which is decided by principal components analysis (PCA), and the efficiency of the K-medoids algorithm is guaranteed by a density-based method because it can select the best start values. The data used was the passenger entry flow of the metro network during five workdays of one week in Beijing, China. By applying this method on the data, the metro stations are clustered into six categories, and the stations are put on the map, so the difference between the stations was determined.

Qinghai Province is rich in tourism resources, so its tourism industry is developing rapidly, becoming the main driving force in local economic growth. The number of tourists on weekends and holidays usually increases significantly, which puts great pressure on traffic and scenic area management. This paper collects data from relevant toll stations and analyzes the characteristics and scale of passenger flow, making a comparative analysis of passenger flow changes, which shows that weather and holidays are the main factors affecting the passenger flow scale. Average passenger flow increase during holidays is over 75%, but during adverse weather conditions, passengers flow drops significantly, by as much as 5%–30%. Through the analysis of passenger flow distribution and its influencing factors, the traffic impact model around the scenic spot can be studied, and data support for formulating traffic organization and management measures during the peak season and holidays can be provided.

This study explores the temporal–spatial characteristics of bike sharing. Survey regions were divided into five zones types: residential, administrative, commercial, tourist, and metro stations in downtown Ningbo. Real-time usage data obtained from 28 survey points showed that usage of bike sharing in evening peak periods was higher than in morning peak periods in terms of temporal characteristics. In addition, usage quantity was slightly higher during weekdays than weekends. In terms of spatial characteristics, usage quantity of bike sharing was higher in metro stations and commercial districts. However, the amount in residential areas was lowest. On weekdays, a usage tidal phenomenon was noted in commercial and tourist zones, as well as in metro stations during weekends. The amount of bike sharing decreased in administrative zones during weekends, but was still observed in commercial and tourist zones. The results of this study provide a reliable basis for urban bike-sharing dispatch management.

This study aimed to assess reasonable supply volume of dockless bike-sharing (DBS) based on limited urban road network capacity. Factors influencing urban road network capacity include size and level of roads, interference, e-bikes, and so on. A time–space consumption model was developed to measure a reasonable DBS size. The model’s parameter values were determined by empirical survey, literature review, and experience. Urban road network capacity was divided into four levels; the ultimate volume was supposed to meet at least level II. The results demonstrated that this model had great practicability in assessing a reasonable size of DBS. The volume of level II was found to be 183,200 vehicles at a turnover rate of 4.0 in Ningbo; however, the current supply volume is worse over a reasonable size. In view of this urgent situation, some suggestions regarding controlling supply volume were proposed to promote DBS use and reduce resource consumption.

With increased construction of urban rail transit (URT) in China, loop-lines are becoming more common and symbolic of the level of rail transit networks (RTN) in various cities. In order to evaluate urban rail transit loops, this paper applies the study of complex networks to URT network analysis, considering both topological and metric properties. The structure of Xi’an RTN is analyzed by using L method, P method, and the fractal model, and the relationship between urban rail network structure and urban passenger flow characteristics is established based on structural features. The results show that the loop increases the connectivity, accessibility, and robustness of its network (effectively improving the transfer efficiency of the whole network), strengthens the connection of the passenger flow distribution points at the center edge, and cuts off relationships between the peripheral areas to reduce traffic pressure in the central area.

The stability of urban bus transport networks has a great impact on urban traffic operations and residents’ daily travel. This paper analyzes the stability of urban bus networks based on complex networks theory. The urban bus network of Shangluo is chosen for analysis. Several featured metrics of complex networks are calculated, including node degree, degree distribution, shortest path, average shortest path, and clustering coefficient. The small-world effects are verified for the urban bus network based on calculation results. Then, the stability of the bus network is analyzed for both random attack and intentional attack mode. Under both random and intentional attacks, the network efficiency is significantly reduced respectively. Nevertheless, the network efficiency declines more quickly under the intentional attack mode. The intentional attack has more significant impacts on the network stability. The analysis results could provide some valuable insights for the bus network planning of Shangluo in the future.

Short-term passenger flow forecasting is an important data support for urban rail transit operation planning; it improves passenger flow organization, gives early-warning for oversaturation, and improves transportation service and safety. Many forecast models have been proposed to forecast passenger flow dynamics. Different models are based on different theoretical backgrounds with different characteristics; moreover, the dynamic of rail transit passenger flow is different in different stations due to land use and location. Six models, namely MA, ARIMA, SARIMA, BPNN, WNN, and SVM models are used to make short-term predictions of passenger flow within five working days in six Beijing stations. After comparative analysis, prediction accuracies of nonlinear models are relatively higher under a similar model input structure. In addition, this work confirms the existence of strong periodicity and stability of passenger entry flow and suggests that these characteristics should be considered to improve prediction accuracy when constructing models in further research.

The traffic modes selecting is the third stage of the internationally accepted and mature four-stage method, using the multinominal logit model (MNL), but the model is sensitive to the multicollinearity of independent variables, and the residents’ travel attributes are mostly multi-collinear, and the requirements for random utility are high, so the applicability of the model is poor. Compared with the multinominal logit regression model (MNL), the support vector machine (SVM), and random forest (RF) in the machine learning method has outstanding advantages for classification problems, while avoiding the shortcomings of the MNL model. This paper is based on«Passenger Flow Forecast of Urban Rail Transit Network Planning in Baoji City in 2016»research data of residents’ travel trips. By comparing the accuracy of the choice of transportation mode of the three models, the support vector machine and the random forest model are more improved in accuracy than the MNL model.

Based on public transport development modes of different countries, this paper studies a combined dynamic evaluation method to promote transit-oriented development under the background of new urbanization, according to the characteristics of urbanization and residents’ travel in China. The index system is determined by the pressure state response model. A combination of subjectivity and objectivity is adopted. The evaluation value of transit-oriented development is calculated according to the weight coefficients and the monitoring scores of evaluation indexes. The advantages and disadvantages of the method are discussed based on the application effect of the method in typical cities. Improvement measures are proposed after analyzing the potential and existing problems of transit-oriented development.

Compared with heavy rail transit systems that have exclusive right-of-way, most trams share right-of-way with other traffic flow at intersections. The method of making operation diagram for traditional rail transit system is not fully applicable to tram systems. Therefore, during the process of generating the operation diagram of trams, a solution based on in-depth study is proposed in this paper for describing trams passing through the intersections. This paper aims to reduce the stop delay of trams at intersections by adjusting travel speed and dwell time. A heuristic algorithm is developed to circumvent the complexity of the solution for the problem described. Finally, the optimization model is applied to a real-world case in Shenzhen, China. The results show that the efficiency and stability of tram vehicles is improved with application of an optimization model. The feasibility of the optimization model is verified by selecting the appropriate evaluation indexes.

Dockless bicycle sharing systems in Shanghai cause unbalanced trips and abandoned bicycles in the street. Few efforts have explored dockless bicycles using stochastic trip and operational data. In this paper, we combine stated survey and data mining for GPS operation data. Six consecutive days of data from 9.93 million records of shared bicycle bands operating in Shanghai were collected. First, trip distance and travel time distribution was compared with stated surveys. Then temporal and spatial variation patterns of shared cycling trips in Shanghai were presented, finding peak periods on workdays and trips mostly to the city center close to metro stations. A clustering algorithm looked for commonalities among the areas of cycling ridership and other geographical features, and classified five groups. Unbalanced trips may result in changes in bicycle supply during peak hours. This study offers suggestions to develop this industry sustainaibly.

During off-peak periods in suburban districts, the bus usually has a large departure interval, and the information of transportation demand and supply is asymmetric, which causes inefficiencies of travel and bus operation. To solve the above problem, this paper proposes a real-time response bus system for non-commuter demands using a mixed-strategy departure condition. The bus dispatching system first selects the buses that have positive benefit combined with heuristic shortest path algorithm and responsive judgement function, then makes bus plans according to estimated buses arrival time and assigns a response bus according to passengers’ selection plan. Finally, this paper takes bus 396 in Chongqing as a case study and verifies the feasibility of a real-time response bus system. The results show that passengers’ waiting time could be reduced by more than 50% when the departure interval increases by 30%, a gain for the bus companies.

In this paper, based on the commuting information of residents, the route optimization of the customized bus is divided into boarding and disembarkation distribution areas. This paper takes the minimum cost of the customized buses as the target to establish a route optimization model for the get-on area and designs an evaluation heuristic algorithm to solve the model. To minimize the driving time cost of the vehicle and the walking time cost of the crowd group, this paper establishes a running route optimization model of single vehicle for the get-off area and uses the genetic algorithm to find the optimal stations. Finally, the number of vehicles running in the distribution areas, the running stations of each vehicle, and the time when the vehicles arrive at each station are respectively obtained by analyzing the examples so customized morning and evening bus schedules can be established.

The unreasonable traffic organization of urban intersections can easily cause a traffic jam, which causes the discharge of pollution gas but also increases fuel consumption. In order to improve the operation efficiency and reduce traffic pollution, vehicle emissions and delays at intersections, this paper analyzes the impact of signal timing optimization and channelization improvement measuring on vehicle emissions. In addition, the paper puts forward the optimization goal of taking delays and emissions as comprehensive indicators using the intersection of Xi’an Yucai Road and North Yanta Road as an example. VISSIM software is used to conduct simulation experiments and compare the detailed data. The results show that the average delay time and the total amount of pollutant emission are largely improved through time optimization and channelization improvement. This study reveals the relationship between traffic organization and exhaust emission, which is of great significance to the overall optimization of intersections.

The congestion and imbalance problem at rush hours exists in many arterials of urban roads. Transit is a highly efficient way to solve the congestion problem due to its high-person capacity, while a reversible lane often used in roads where the traffic flow differs from morning to night. Taking advantage of the transit priority method and the reversible lane method, this research proposes a comprehensive optimization method. The method combined dedicated bus lane assignment, general lane assignment, and intersection signal timing in a unified framework. A numerical analysis based on the surveyed data of Siping Road is conducted to illustrate the effectiveness of the proposed method. Compared with the implemented reversible lane method, the proposed method can decrease the degree of saturation and increase the capacity of intersections. This research also presents the lane assignment plans under different traffic flow demand.

Urban bus operation is mainly affected by fluctuations in passenger demand and travel times on the route. Current studies try to find the optimal bus schedule plan with a fixed bus vehicle size. However, this method is not optimal to tackle the stochastic factors of the bus system. The aim of this paper is to develop a new schedule plan optimization model for the bus line with multiple vehicle sizes. The dispatching time of different vehicles are taken as the optimization variables and the total cost of passenger travel time and bus corporation operation minimization are set as the optimization objective. The impacts of bus crowding level and intersection signal control plan on travel time are quantified. Finally, the proposed method is validated using a bus line in Harbin City, China. Comparisons are conducted between the proposed method and traditional method.

Based on the research background and significance of the integration of urban and rural public transportation, this paper analyzes the problems encountered in the current integration of urban-rural public transportation reform process and presents the planning principles and planning content. Focusing on the network, hub stations, tourism public transit, and operation guarantee, this paper proposes the development mode to integrate urban-rural public transportation and uses the case of Rizhao’s reform to explore the facility planning, operation management, and related countermeasures for urban and rural public transportation.

Whether traffic organization in a railway passenger transportation hub is efficient is directly related to the connection among transportation modes within the hub and the degree of operational efficiency. Analyzing the characteristics of passenger transport hubs, combined with the common traffic problems in the hub, passenger transport traffic organization principles are proposed. Optimization of traffic organization of railway passenger transport hubs through several internal and external transportation and traffic organizations was researched. Finally, using the elasticity coefficient method to predict passenger flow at Zhenjiang South Station and considering the multiple Logit model and generalized travel expenses, the three parameters of fare, comfort, and running time were selected as utility function tokens to solve the transfer sharing rate. Studying the layout of the facility, transfer streamlines, traffic organization, and sign induction of the station provided reference for the optimal design of traffic organization for the same type of hub.

Looking at the problem of railway tunnel entrance collapse, this paper proposes a warning system based on intelligent video surveillance. In the image processing algorithm, HSV color space integrating RGB time domain segmentation combined with double threshold image segmentation technique is used to extract the moving object in the monitoring range. The Sobel edge detection operator and the improved Hough transform algorithm are used to extract the characteristics of the rails to determine the size and position of moving objects. Self-adaptive background update mechanism based on Kalman filter theory eliminates the influence of natural conditions such as weather and light. Feature recognition of collapse is realized based on the image processing algorithm. The slope displacement monitor is introduced to achieve real-time early-warning before collapsing. Simulation experiments show that this method can extract the target features accurately, achieve immediate and effective early warning, and cope with the environmental disturbance factors.

Since urbanization and economic globalization is speeding up, the rapid expansion of cities and surrounding satellite towns has become a trend with universal meaning, forming metropolitan circles and inter-city areas. A new regional unit needs different forms of transport links. The construction of regional railway lines in some big cities abroad is often for the purpose of reasonably guiding the urban expansion, while at present there is a lack of a suitable transportation system between high-speed railway, intercity line, and metro lines in China. Based on the principle that regional transit needs to play the role of cohesion and outreach, this paper studies the transportation network based on the urban form, so as to determine different function orientation and guide the key technical indicators of regional transit.

The profit of railway passenger transportation management (RPTM) is determined by (1) the cost, which is mainly related to the supply schedules (e.g., line plan, timetable, and crew schedule), and (2) the income of ticket sales in the reservation horizon, at the beginning of which a ticket allocation plan is to be made based on a timetable. Generally, the supply schedules and the ticket allocation plan are made separately. This may lead to the loss of global optimality in terms of maximizing the profit of RPTM, since a supply schedule with higher cost may yield more profit. This paper fills this gap by proposing an integer linear programming model, integrating the line plan model, and ticket allocation model. A real-world case is conducted to prove the efficiency of the integrated model, bringing a 25.4% increase in profit over separate models.

The development mode of compact cities caters to the concept of green sustainable development. With the planning status of urban cluster development, the development mode of compact urban agglomeration emerges. To improve the traffic compactness of urban agglomerations, this paper proposes a rail rapid transit trunk construction method for the development mode of the compact urban agglomeration of the great western triangle economic zone (abbreviated as GWTEZ). First, this paper comprehensively studies the area and its neighboring transportation node cities, and uses the complex network to select the central target city. Then, the research is carried out by combining the urban development potential mode with the graph theory minimum spanning tree Kruskal algorithm. In the end, the rail rapid transit trunk lines of GWTEZ are rationally constructed. The results show that the rail rapid transit trunk lines presented here conform to the compact urban agglomeration development mode.

The train occupancy rate (TOR) is an important indicator to measure the matching relationship between supply and demand of high-speed railway passenger transport. The railway passenger transport department usually evaluates the actual driving effect of the train diagram by TOR. Before the actual timetable is executed, the TOR is unknown and it is difficult to describe it with an accurate matching model. In recent years, with the rapid development of China’s high-speed railway, the available operational data has increased dramatically, which has prompted us to no longer use traditional applied mathematics methods, but to use deep learning methods to improve the accuracy of predicting TOR. In this paper, long short-term memory (LSTM) is used to predict TOR. The specific methods of feature extraction and data input are discussed. Considering the variation law of passengers under multi-factors, the operation data of Beijing-Shanghai high-speed rail between 2012 and 2018 is utilized to verify the effectiveness of this model. The final experiments show that the results obtained in this paper are better.

The lack of understanding of ridership evolution frequently causes emergency operation plans to fail when ridership increases suddenly. Based on a multiple regression model, this paper focuses on the urban rail transit ridership evolution under rainfall. The first stage explores the rainfall-ridership relationship. The second stage establishes a multiple regression model using the hourly ridership and the current hour, the current hourly rainfall, the previous hourly rainfall, the previous hourly ridership, and the rainfall in the previous period. The third stage uses the rainfall and ridership data in Nanjing in 2016 and 2017 to train and test the model and confirmed that the ridership prediction of the multiple regression model is close to the actual ridership. The results can predict future hourly ridership at stations and support transportation system management to allow operation plans to be adjusted according to predictions.

The influence of the opening of the high-speed railway from Beijing to Shanghai on the spatial interaction between cities in Jiangsu province is studied here using the improved gravity model and the breaking-point formula to calculate the relevant data of the cities in the year before (2010) and the year after (2012) the opening of the Beijing-Shanghai high-speed railway. The spatial interaction between cities in Jiangsu and the total amount of external attraction of each city are analyzed by ArcGIS. The spatial interaction between cities in Jiangsu is further analyzed by vertical and horizontal comparisons. The conclusions show that the opening of the Beijing-Shanghai high-speed railway had a significant impact on the region, and increases competition among cities in Jiangsu while promoting coordinated urban development, which provides support for the development of high-speed railway in the future.

With the development of subways, safety of subways requires equipment for fault diagnosis technology. This paper briefly expounds on the construction principle of Bayesian network and the propagation and update of network beliefs and elaborates the process of transforming fault tree to Bayesian network, including the mapping relationship between nodes and events and the mapping relationship of logic gates. Finally, the fault tree of the subway door system is established, and the fault tree is transformed into the faulty Bayesian network model. The case analysis of the Bayesian network accurate reasoning theory based on the junction tree algorithm is carried out.

With the increase of economic exchanges between cities, interurban trains have become the backbone of the public transportation system of urban agglomerations, playing a leading role in passenger flow organization. Based on the logit model, with data from passenger flow forecasts of the interurban railway of Central Henan urban agglomeration, this paper forecasts the passenger flow of the interurban railway through the improved four-stage method, and predicts the passenger volume of Zhengzhou-Jiaozuo route and Zhengzhou-Kaifeng route in 2020 and 2025, in order to summarize the experience for the future interurban development of Central Henan urban agglomeration.

The Shanghai high speed Maglev demonstration line has been in operation for 16 years and has entered the middle stage of design life. The tolerance capacity of external random collision damage is an important capability of mechanical parts of running mechanisms. The running mechanism of a Maglev system gradually produces fatigue damage under the condition of time history and cyclic load, which will reduce its ability of external random impact damage. Based on fatigue cumulative damage theory and fracture mechanics, the damage tolerance life of important parts of vehicles running mechanisms under degradation strength was studied. A stress-strength interference model under the degraded strength of important components was built, and the factors that affect the remaining life of a defective part under performance degradation was studied. The above research provided theoretical support for selecting the materials for the mechanical components of the running mechanism of Maglev system.

The “Rejuvenation” high-speed train is an advanced electric multiple unit (EMU) train developed by China independently. When high-speed trains have been put into operation, situations where the auxiliary power supply system cannot be used because of battery system failure are becoming more common. This poses a great threat to high-speed train safety and stability. This paper researches the actual operating state of the auxiliary power supply system of the “Rejuvenation” train. Each sensor of the system is used as network node to construct a Bayesian network. We use the I-ACO-B algorithm to learn structure of Bayesian network, and use the Bayesian parameter estimation algorithm to learn parameters of Bayesian network. Through these two kinds of learning, the auxiliary power supply network, can be constructed. Finally, the network can be verified by actual data.

Considering the influence of running conditions on the running performance of high-speed rail vehicles, the global optimization of rail vehicle suspension parameters can improve the adaptability of rail vehicles under multiple running conditions. The parameters that have a significant effect on the rail vehicle running performance are selected via the sensitivity analysis of rail vehicle suspension parameters. Taking account of four typical running conditions in the process of rail vehicle running, the rail vehicle running performance surrogate model under multiple running conditions based on the RBF surrogate model method is employed. This study vitally provides the theory and databases for the global optimization of suspension parameters and has a certain reference significance to improve the running performance of rail vehicles.

Emergencies on urban rail transit (URT) rely on understanding passenger flow. It is necessary to study the characteristics of passenger flow and determine the scope of the emergency in a timely and reasonable fashion. This paper uses an impact analysis method of passenger volume under emergent condition of URT using automated fare collection (AFC) data. Based on the method, the affected scope is determined, and the analysis methods of the characteristics of passenger flow within emergent impact scope can be proposed. Lastly, combining with AFC data of Beijing URT, this paper analyzes spatial and temporal distribution characteristics, change characteristics of passenger volume at key stations and the number of stations affected by the emergent event, and summarizes passenger flow change rule under emergent condition. This quantitative analysis method has universal applicability in providing support for distribution of passenger flow, predicting passenger flow, and early warning of URT emergency.

Based on the centralized monitoring data of the ZPW-2000A joint-less track circuit, this paper realizes the early warning for the frequent faults of the track circuit of the high-speed railway. According to the mapping relationship between the monitoring data and the basic equipment faults indoors and outdoors, the data trend analysis method is used to predict the monitoring data in the future period from the historical data, and warning about possible faults in the track circuit equipment can be caught early. This data tend analysis method plays a positive role in the implementation of comprehensive maintenance and intelligent operation and maintenance of modern railway.

This study uses a real expressway project with poor visibility in off-ramp areas. A simulator collected driving behavior data from testers that drove from the main road to the off-ramp under general or special design of the exit guidance system. The results show that the drivers change lanes to the outside lane earlier and more smoothly before the poor visibility off-ramp under the special design. Maximum deceleration behavior occurs before the off-ramp action point in the outside lane. Also, the special design reduces the impact on the main traffic flow. However, emergency braking happens in the 100 m range of the outer lane in front of the action point, which impacts the lane’s main traffic flow. The specific design of the guidance system can improve driving safety away from the main lane, but further study is needed to determine if it can help main traffic flow in the left two lanes.

After lane expansion and design speed increases for expressway reconstruction, the clothoid curve of curve section without clothoid curve should be added, resulting in safety benefits and investment costs. The United States, Germany, and Japan agree that when the curve radius reaches a certain threshold, the clothoid curve cannot be set. The radius threshold of circular curve without clothoid curve in Chinese specifications is the minimum radius of circular curve without superelevation. The radius is large and conservative. The design criterion research of expressway clothoid curve was developed to meet actual engineering needs. The driving simulation test with different curve radius sections before and after the clothoid curve is carried out. Based on the stability of entry and exit circular curves, design criteria for clothoid curve is proposed. The radius threshold of circular curve without clothoid curve is determined by combining foreign specifications and driving simulation test results in China.

The paper deals with the design method to solve drainage difficulties and reduce the maximum of longitudinal slope in the superelevation transition for oval racing track. The cross section linear equations are established to suit for drainage in different sections. The intervals are divided with the variation characteristics of the superelevation transition for oval racing tracks. The continuously varying rotation axis for superelevation transition and the constraint equations of cross section are proposed based on different intervals. The design method can be accurately used to solve the constraint equations and the longitudinal slope with the aid of a computer. Finally, they provide very good practicality when demonstrated by taking Bloss curve as an example. The research results of the paper have important practical value for designing the superelevation transition for oval racing tracks. They have certain reference value for the future research of super highway with design speeds of over 120 km/h.

Due to the diversity of natural environment, traffic composition, economics, and social impact worldwide, the policies and specifications of highway design appear diverse around the world. In this paper, seven countries were selected to make comparisons to illustrate the similarities and differences. There are many similarities in fundamental design principles, however, differences exist as well, such as vertical radius, grade, etc. To discuss the effects brought by these differences, further comparative research was made by means of computer simulation. Carsim and HintCAD were utilized in this paper. Based on various design theories and technical indexes, several representative highways were designed, the vehicle and driver’s models were established. After that, the corresponding simulation parameters can be acquired and used for checking the rationality. This research can provide a better understanding of design concepts and may facilitate the flexibility for highway geometric design.

With the progress of science and technology, existing methods of surveying and safety auditing have been difficult to meet the increasing demands of people, so a new method is needed. First, by comparing and analyzing the advantages and disadvantages of different mapping techniques, we finally find that under the different demand conditions, the digital mapping technology which uses the UAV aerial mapping, total station, and 3D LIDAR on vehicle in collaboration with the GPS is more advanced, accurate, and practical. Then, with the method of HintCAD in collaboration with 3D Max to realize the digital visualization of road design, the designer can analyze the road design in 3D. Finally, a driving simulation is based on operating speed and human factors to audit the safety of road design, which can offer a truly people-oriented safety audit from the perspective of road users.

An airport’s terminal curbside is a passenger transition from ground transportation to air transportation or vice versa. It is an important bottleneck restricting the development of airports. Reasonable curbside scaling can alleviate serious congestion conditions effectively. This paper proposes a terminal curbside length model, and then takes Beijing capital airport’s terminal 3 as a case to verify the effectiveness of the model after analyzing the characteristic of traffic flow. Recommendations of terminal curbside lengths under different traffic condition are obtained by numerical simulation. The model makes up for the deficiency of the design method of airport’s terminal curbside and provides support for terminal’s curbside geometric design.

This paper examines the optimal lane width of the urban expressway by using a driving simulator. Vehicle speed and lateral offset were selected as indicators of driving safety, and limited speed and free speed experiments were carried out. The results show that lane width has a significant effect on lateral offset but not on vehicle speed. According to the lateral offset, when the speed limit is 80 km/h, the reasonable lane width is preferably greater than or equal to 3.25 m. When the speed limit is 100 km/h, the reasonable lane width is preferably greater than or equal to 3.5 m. According to the in-lane driving proportion, 3.25 m can meet the requirements of normal driving. In order to satisfy driving safety, the lane width can be compressed to improve the user efficiency of the urban expressway.

Longitudinal acceleration of a vehicle represents the change of driver’s speed, and the drastic change of speed indicates undesirable designs. However, for the adjacent combined sections, there is still no scientific and effective acceleration prediction method. To accommodate this limitation, a new modeling approach based on frequent and equidistant longitudinal acceleration measurements was proposed in this paper. The Tongji University driving simulator was used to conduct the experiment and collect free-flow longitudinal acceleration data. Longitudinal acceleration profile was derived and grade, curvature, etc., extracted from upstream and downstream segments were used to estimate a generous longitudinal acceleration prediction model. The model results show that the average slope, slope difference, and average curvature difference between the upper and lower reaches is positively correlated with the longitudinal acceleration, and the average curvature value is negatively correlated with the longitudinal acceleration.

As a non-destructive testing (NDT) technique for disease detecting in pavements, ground penetrating radar (GPR) has garnered increasing attention. However, it is still a problem to determine which kind of disease is easy to detect. This paper investigates the utilization of the finite element method (FEM) software COMSOL to model the propagation of electromagnetic waves in pavements. Two quantities of interest were investigated and analyzed numerically: depth and radius. The amplitude is used to determine the complexity of finding the disease. The result is that the disease with larger radius and lower depth can be recognized easily.

In order to improve the efficiency of traffic management in urban areas, a recognition method of abnormal vehicle behaviors in urban traffic scenes using combined features of histograms of oriented gradient (HOG) and local binary pattern (LBP) is proposed in this paper. Firstly, three feature extraction methods, such as histograms of oriented gradient, local binary pattern (LBP), and edge orientation histogram (EOH) are analyzed, comparatively. Secondly, the experiments of abnormal vehicle behaviors, such as normal driving, running a red light, pressing line, and illegal vehicle steering, are conducted using combined features and support vector machines. Results showed that the combined features of HOG and LBP offers the best classification performance, and the recognition rate is over 93.6%. With combined features of HOG and LBP, the classification accuracies of illegal vehicle steering are over 82.7% in the experiments.

Accurate and reliable vehicle location is a key component of numerous ITS applications. The accuracy of the existing positioning cannot meet the requirements of strong adaptability and high accuracy for the lane-level positioning in the VANET environment. This paper proposes a lane-level vehicle positioning method based on MM (map matching) and RSSI (radio signal strength indication)-DR (dead reckoning). It adopts the hierarchical positioning method: firstly, use MM to realize the rough positioning; secondly; then use RSSI and DR to obtain the relative positioning; and finally, use data fusion to realize the lane-level positioning. The numerical simulation of the whole model is carried out and the optimal weight combination for fusion method is obtained. The average positioning error, lane positioning accuracy, and trajectory difference of different positioning methods are compared to verify the effect of fusion method.

The dynamic characteristics of heterogeneous traffic flow comprising regular and connected vehicles have drawn increasing attention recently. However, the existing car-following models in connected environments commonly lack experimental data as evidence. Based on field data mining analysis results, an extended heterogeneous car-following model is proposed to investigate the effect of DSRC (dedicated short range communications)-based safety messages on traffic flow stability in this paper. The analytical stable condition of this proposed model for homogenous and heterogeneous traffic flow is obtained by stability analysis. Finally, numerical simulations are carried out to verify the theoretical analysis results. Both analytical and simulation results show that when multiple preceding vehicles’ safety messages are taken into account, both traffic flow safety and stability can be improved. In addition, the traffic flow stability is enhanced with the increase of market penetration rate of connected vehicles.

With the rapid development of China’s economy, traffic congestion has become a serious problem affecting the efficiency and safety of the traffic system, especially in urban regions with high traffic density. Due to the lack of effective forecasting methods, traffic congestion events seriously affect normal operation of the intracity traffic network. In order to achieve better prediction results, a type of gated recurrent neural network—long short-term memory neural networks—were used to build the model. The prediction accuracies for different tasks all approach 85%. Then, several different factors which may influence the congestion prediction were analyzed to find why LSTM could not fit the congestion change better. In order to have a comprehensive understanding of the model based on the LSTMs, several algorithms were studied by building models. As the result, the prediction accuracies of these new models are noticeably lower than those of the LSTM models.

Vehicular crowdsensing has attracted lots of attentions due to its low cost and timeliness for urban sensing applications such as traffic estimation. It is of great importance for a vehicular crowdsensing system to recruit a limited number of vehicles to achieve a maximum sensing coverage and get useful traffic data of roads. It is challenging due to the unpredictable behaviors of vehicles. In this paper, an efficient vehicle recruiting scheme is proposed based on the road importance in road network. We evaluate the performance of the proposed algorithm through detecting the traffic jam of the road network, which is implemented by simulation. The results demonstrate that the proposed algorithm outperform existing algorithms on the coverage and improving the road network detection accuracy.

Recognition and prediction of urban traffic states are vital for congestion mitigation for the government. In this study, a trajectory dataset covering an area with 6 km² in Chengdu was used. First, the area was divided into unified 100 × 100 m grids for convenience of aggregation. For each grid, several predefined traffic parameters were extracted based on the coordinate sequence of each car. After that, PCA (principle component analysis) was performed on the feature matrix to reduce dimension. K-means algorithm was utilized for acquiring traffic state clusters. On the basis of the clustering results, a CNN (convolutional neural network) prediction model was established for traffic states prediction. Results are as follows: (1) three different traffic states are generated, which are quite diverse with regard to the distribution of traffic parameters; (2) evolution process of traffic states was analyzed on two different scales; and (3) the prediction accuracy achieved 85% for speed prediction model.

Characteristics of the area of the vehicle front windshield, including decorations and labels, are important for vehicle retrieval, and how to detect the area of vehicle front windshield is an interesting topic. First, a novel area detection method of the vehicle front windshield, including vehicle detection and front windshield ROI positioning, is proposed. Second, several other area detection methods of vehicle front windshield are analyzed comparatively. Finally, the comparative experiments are carried out, and the experimental results indicate that the proposed area detection method of vehicle front windshield offers the best classification performance, the recognition rate of which is over 90.70%.

For obtaining more accurate car-following data, this study describes the design, implementation, and application of a new real-time car-following data collecting method based on a binocular stereo vision system. The car-following data consists of car-following distance, velocity, relative velocity, and driving trajectory. The system uses triangulation principle to calculate car-following distance. The system can calculate car-following distance and store the related data in real time. In the case of stable measurement, this research analyzes that the mean absolute percentage error of the distance measuring system is less than 5%. In order to solve the measurement instability caused by environmental interference, this study proposes a new state estimator based on an extended Kalman filter (EKF) technique, in which microscopic traffic flow model is used as nonlinear dynamic model. The results indicate that the proposed EKF significantly improves the data reliability in real-time car-following data collecting process.

Transportation mode split is the core part of the four-stage method and its characteristics are constantly changing. However, most transportation mode split methods adopted in traffic planning assumed that each area had the same traffic patterns, which is not correct. This paper used random forest to split the transportation mode. First, several features were extracted from resident trip survey data and mobile phone signaling data. Then, the model accuracy was adjusted using a variety of methods. Next, the mode split model was built on the random forest. Finally, mobile phone data was used to verify its accuracy. The results show that the random forest performs well in the transportation mode split with 85% accuracy and it could be used for mobile phone signaling data to identity the transportation mode.

Unscented Kalman filter (UKF) depends on system model in indoor vehicle positioning. However, the constructed model does not always agree with the actual model because of the inherent error due to lacking complete cognition for the target system. Gaussian process regression based square root UKF (GPSRUKF) can lower the error and improve estimation precision. The proposed method is based on the fact that Gaussian process regression learns the deviation between the model and the actual sampling data. Additionally, square root UKF (SRUKF) overcomes the defect that UKF requires more numerical stability for state covariance updates which need to be re-factorized to obtain the sigma points set in every iteration. Experiments on indoor vehicle positioning shows GP-SRUKF has a better performance by compensating deviation comparing with SRUKF and UKF only in nonlinear systems when we build the target model by sampling data in advance.

Vehicle retrieval-based features is an important topic in intelligent transportation systems. Firstly, a novel method of vehicle retrieval using color histogram features of vehicle front windshield is proposed in this paper. Secondly, the proposed vehicle retrieval method is compared with local binary patterns: gabor wavelet transform, contourlet transform, and scale-invariant feature transform. Finally, the comparative experiments are carried out, and the experimental results indicate that the proposed vehicle retrieval method offers the best classification performance, and the comprehensive index of search of vehicle retrieval using color histogram features of vehicle front windshield is 86.7%.

Automated driving could reduce driving workload, enhance traffic safety, and improve traffic efficiency and has become a research hotspot in recent years. Numerous studies have looked at the effect of automated driving on capacity, mostly focused on highway driving. However, travel behavior occurs in the road network, which consists of a large number of different scenarios, such as expressway, merging/diverging area, trunk road, branch road, signalized intersection, and is different from highway driving. To evaluate the effect of automated driving more realistically, this paper simulates road network level, and investigates the influence of automated driving under different conditions. Results show that implementation of automated driving does not necessarily improve efficiency. Meanwhile, the effectiveness is related to multiple factors. Vehicle-infrastructure co-operation should be considered in future application, so as to make better use of spatial-temporal resource of road networks.

With the continuous development and maturity of the application of computer and wireless communication in the field of transportation, the development of autonomous vehicles has also made remarkable achievements. The present research mainly focuses on how a single vehicle operates autonomously without a driver, but less research on the traffic flow consisting of autonomous vehicles has been done. This paper aims to simulate cooperative adaptive cruise control (CACC) vehicles, which is the significant phase to completely self-driving, setting new rules for car-following, and lane-changing vehicles by modifying the external driver behavior model of PTV VISSIM. By comparing the differences between conventional traffic flow and CACC flow, some conclusions are drawn to support further research. This paper concludes with three new findings on the characteristics of CACC flow for further research.

Geographic division directly affects regional traffic flow prediction result. Classical methods like latitude-longitude meshing and POIs (point of interest) clustering can’t effectively describe traffic transfer between regions. In this study, a topology map is generated by road segment clustering. Several road segments in a network are the basic clustering units. The adjacency, connectivity, and congestion between road segments and the congestion degree of road segments are made as attributes of the cluster similarity measurement. Road segments within urban areas are clustered by spectral clustering algorithm. Topological map composes converged clustering centers and edges made of connection ports among the roads of each node. The experiment data consists of 120,000 road segments and vehicles’ trajectory dataset in Beijing. Topological maps are generated at different granularities. The results show that the topological map has a better visualization and the amount of traffic flow information is superior to POIs clustering and meshing.

Traffic simulation is an important method of traffic study, and the accuracy of traffic simulation results depends on the accuracy of traffic model mapping real traffic conditions. The vehicle decision model in the virtual traffic simulation environment is an important step in traffic simulation. Taking into account the actual changes in deceleration of the vehicle, based on the principle of the Gipps vehicle following the model, this paper constructs a vehicle decision model that is more in line with actual conditions. The model in this paper includes two aspects: the vehicle following decision and vehicle lane change decision. The purpose is to make the vehicle’s decision in the virtual traffic scene more in line with the actual situation.

Deep learning approaches have recently demonstrated the ability to predict traffic flow with high accuracy, based on research of short-term traffic flow prediction methods, especially the long-short term memory (LSTM) neural network. However, the potential of LSTM in traffic prediction has not yet been fully exploited in terms of the depth of model structures, lack of multidimensional data, and domain knowledge fusion. In this work, a short-term traffic flow prediction model with the time series decomposition analysis and LSTM neural network is presented. The combination of in-depth information mining of traffic data and LSTM neural network is more reliable than the separate application of LSTM neural network. The effectiveness of the proposed method is validated and compared with other classical statistical and state-of-the-art neural network models, which indicates the proposed method achieves superior prediction accuracy.

Despite the highly promising future of connected vehicle technology, its communication through the wireless network in an open-access environment is vulnerable. Many previous studies focus on clarifying attack types and security frameworks but very few on the spreading of malicious information generated by cyberattacks. Therefore, it is necessary to understand the propagation pattern of malicious information. To this end, this paper introduces an analytical model, named vehicular malicious information propagation (VMIP), which integrates a classical epidemic model with a signal fading model using velocity and position information of platooned vehicles. The analytical and numerical results show that the proposed model can express the interactions between traffic dynamics and malicious information spreading. This paper is expected to contribute to a better understanding of the malicious information propagation in connected vehicles and lays a foundation for the future development of control strategies on mitigating the disastrous effects of cyberattacks.

Mobility as a service (MaaS) is the integration of various forms of transport service into a single mobility service accessible on demand. In order to improve the attractiveness of MaaS service, based on the rapidly flourishing development of mobile internet inundated with all kinds of consumer services, such as eating out, travelling, movie watching, entertainment, etc., this paper proposed an innovative business model that integrates MaaS service with the internet consumer services through the provision of discounted mobility service by consumer service providers after booking or finishing the consumer services, and technically realized through a QR code produced by a mobile app, which will empower both types of services, increase the volume of MaaS service while attracting and enlarging more consumers, and at the same time, also relieving the government’s financial subsidy pressure on public transportation services.

More and more non-motorized vehicles (including bicycles and electric bicycles) are pouring into the intersections, making the intersections’ environment more complex. The traffic accidents related to the vehicles and non-motorized vehicles at the intersections are serious. In this paper, vehicle trajectory sets are extracted at the intersections by using the video detection technology. The trajectory prediction model of motor vehicles based on the long short term memory neural network training is obtained, which consider the influence of non-motorized vehicles. The trajectory prediction model based on LSTM is used to predict the trajectories of the vehicles passing through intersections. The overall approach was tested on real trajectories sets at specific intersections and results show that the model has a high success rate and the final trajectory prediction has a better accuracy.

Bike Sharing is an important travel mode in urban areas. We aim to detect the potential of the travel-mode shift from cars to shared bikes, and explore its possible influences. We propose a binary travel-mode choice model and consider travel cost of private bikes as a function of usage frequency, which will be distinguished from shared bikes. Based on the differences and similarities between private bikes and shared bikes, the choice model calibrated by private bikes and cars can be used in the mode choice between cars and shared bikes, where the travel cost of shared bikes is different from private bikes. We made a case study, using Beijing’s travel data to calibrate a binary choice model between cars and private bikes and predicted the mode shift from cars to shared bikes. There are several quantitative results and relative qualitative explanations, describing the scale and impacts of the travel-mode shift.

Real-time and accurate short-term traffic flow prediction can effectively improve the efficiency and safety of the transportation system. However, complex traffic systems are highly nonlinear and random, which makes short-term traffic flow prediction a challenging issue. In recent years, deep-learning based methods have been widely applied in short-term traffic flow prediction. Particularly, the long short-term memory neural network (LSTM) model bears great potential for its capability in learning from temporal information. In this paper, an improved LSTM model is used to predict the short-term traffic flow of a target road section of the East 4th Ring Road of Beijing, and to analyze the influence of different input configuration on prediction accuracy as well. Experimental results demonstrate that feeding upstream flow and velocity information does improve its overall performance. Especially after traffic flow information is fed with corresponding temporal information, the accuracy of traffic flow prediction has been significantly improved.

Poor visibility in bad weather has significant impacts on traffic safety. An ability to accurately monitor visibility can help reduce the driving risk under this condition. However, visibility measuring based on sensors are precise but costly, and measuring based on vision are lower cost but more complicated in training and deployment. This paper proposes a simple yet effective method to estimate visibility using roadside cameras. The method is established based on the definition of visibility in meteorology, taking a great distance at which an object can be clearly discerned from a bright background as the visibility. The method takes the sky region as the reference background image and using relationship mapping for the distance measurements. This proposed method simplifies the calibrating and measuring process, visibility estimation can be obtained without training or configuration, making it easier to use in practice.

With the popularization mobile Internet, cloud computing, big data, artificial intelligence, and other new information technologies, the advantages of traditional transport enterprises based on resource monopoly are diminishing. MaaS (mobility as a service), as a quite novel and emerging mobility concept, brings a new business model and ways to organize and operate the various transport options, providing a new opportunities to serve unmet demand for transport operators. In order to speed up the development of MaaS in China, this paper firstly summarizes international research results and practical cases and analyzes their reference to the development of MaaS in China. Secondly, this paper suggests some of the most critical fundamentals and actions for the relevant stakeholders involved in the MaaS ecosystem in China. Finally, this paper suggests some crucial indexes to assess the readiness of a city for MaaS implementation in China.

Urban traffic road environments are complex and we present a vehicle detection method based on symmetrical features for vehicle contour and license plate. First, the vertical symmetry axis of vehicle contour is detected by a horizontal scanning line, and the horizontal and vertical symmetry axes of the license plate are detected. Finally, the vehicle area is located according to the license plate symmetry axis. Several vehicle detection methods in urban traffic scenes, such as those based on symmetrical features, license plates, gabor features, and support vector machines (SVM), and Haar-like features and AdaBoost classifiers are compared in this paper. The theoretical analysis and experimental results show that the method of vehicle detection based on symmetrical features effectively eliminates environmental influences such as noise and road markings on vehicle area detection. The detection rate of this method is superior to three other methods and the detection accuracy is up to 94.2%.

In this paper, three traffic prediction models based on deep learning are used to predict the traffic flow of capital airport. First, we reconstruct the washed traffic flow data to make the prediction results spatial-temporal. After smoothing and standardization, the characteristics of airport traffic data are studied using the stacked automatic coding machine (SAE) model, the long and short memory network (LSTM) model and the control gate recursion (GRU) model, and the final results are predicted by using the regression layer on the top layer. Finally, the results are obtained by anti-standardization, and the three models are obtained. We then compared the reliability of the three models and proved different loss functions.

Understanding the non-linear relationship between traffic injury severity and factors in accuracy can help decrease accident occurrence and improve driving safety. This paper uses a GA-BP neural network to model the relationship and predict injury severity in traffic accidents classified into fatality, serious crash, and slight crash. And it validates the superior performance of GA-BP with crash data from the UK in 2015, compared to the BP neural network and the logistic regression model. A sensitivity analysis is applied to find out the contribution that input variables have on injury severity. This paper indicates that the GA-BP neural network provides a reference for injury severity prediction in traffic accident.

With the expansion of urban space, many new districts emerge outside the central city. Large cities then face new traffic problems as the traffic demands between new districts and central city increase. Under limited road resources, most cities have committed to rapid transit development. Because there are several kinds of rapid transit modes that can be selected for an urban corridor, the government or the decision-making department should choose the most suitable rapid transit mode based on its actual demand. This paper provides a quantitative method based on value engineering. With the establishment of a functional index and life circle cost index, the value index of various transit modes was calculated, thus providing a quantitative decision basis for rapid transit mode selection in urban corridor. This approach was illustrated by a case in Zhenjiang New District in China, and proved to be reasonable.

The implementation of the “vehicle-to-pedestrian” policy has greatly reduced the capacity of roads, especially in the two-phase signal intersections without dedicated phase of right turn, By using the parking line method, acceptable gap theory, and video survey, the capacity of the intersection of Yanta North Road and Jianshe West Road in Xi’an City is analyzed quantitatively. A simulation of VISSIM is carried out by changing the pedestrian traffic volume, using queue length, and the number of stops as evaluation indexes. The results show that the traffic volume of pedestrian has a significant impact on the traffic capacity of right turning motor vehicles. The research results can provide relevant ideas and theoretical basis for the improvement of signal intersections.

In order to evaluate safety impacts for local areas suffered but not served by a multi-lane highway, the problem of community severance caused by a multi-lane highway with high speed and controlled access is researched. Conflicts between local traffic and highway traffic cause the problems of safety and mobility. A comprehensive assessment method is implemented to value the impact for local trips’ safety caused by community severance. The negative benefits brought by detouring distance, traffic volume, width of cross-section, and speed of highway are measured as quantitative or categorical variable, in order to evaluate safety impact of access-management perspectives. The aim of the research is to remind designers, planners or decision-makers of highways to consider the community severance problems for local trips.

Congestion on road sections is concentrated at the signal-controlled crosswalk, which is characterized by long waiting times for pedestrians crossing and slow traffic for vehicles. In view of this problem, we proposes a long-inductive actuated pedestrian crossing signal control for road sections, which move push-button ahead in order to save time for pedestrians. The distance between the push-button and the crosswalk is called the long-inductive distance. Long-inductive distance can make the pedestrian walking time offset by the extension time, which intend to reduce the waiting time of pedestrian crossing. Three long-inductive distance calculation methods are established and tested in VISSIM. The VAP module of VISSIM is used to code the scenarios. Results have shown that the long-inductive distance which utilizes vehicle green extension time and pedestrian walking speed shows the best performance in reducing the pedestrian waiting time when compared to fixed-time signal control and other actuated pedestrian crossing control.

Some traditional control algorithms such as ALINEA and PID have been widely applied for highway ramp metering control. However, in many practical cases, bottlenecks with smaller capacity than the merging area may exist further downstream for various reasons. This paper focuses on distant downstream bottlenecks with two suitable algorithms: (1) fuzzy self-adaptive PID controller; and (2) PI-ALINEA, which is a proportional-integral (PI) extension of ALINEA. In simulation studies, this paper uses the cell transmission model, which is a differential discrete form of the kinematics model. The two algorithms are fully compared under different distant downstream bottlenecks. As shown in the result, fuzzy self-adaptive PID controller has a better control effect than PI-ALINEA.

On the surrounding roads of some middle schools, there are many pedestrians, non-motor vehicles, and motor vehicles gathering together or travelling during the peak hours. This increases the traffic pressure, leads to the confusion of traffic order, as well as the deteriorated traffic safety situation. A case study on the traffic organization of the surrounding roads of a middle school was carried out. On the basis of the traffic survey of the nearby intersection and roads of the school and data analysis, the relative problems were identified. The corresponding traffic organization optimization plan were proposed. By means of the traffic simulation, the traffic operation situation and traffic safety situation before and after the implementation of the optimization were analyzed, the feasibility and the effectiveness of the traffic organization optimization plan were then verified.

By adjusting the parameters of signal lights at arterial intersections, arterial green wave coordination control can make certain vehicles travel through the downstream intersection without or with fewer stops, and thus improve traffic capacity. This paper takes three intersections in Jingtang Road as examples, evaluates the control strategy, and concludes that the bandwidth in the morning peak is 118 s (northbound 59 s and southbound 59 s), and that in the evening peak is 103 s (northbound 51 s and southbound 52 s). Subsequently, based on the VISSIM software, the situations of the arterial and intersections before and after optimization are evaluated. The result shows that, after optimization, the travel time in arterial reduces by 26.2%–59.0% and the average speed increases by 35.5%–144.0%; the average delay in intersections reduces by 46.7%–82.2% and the delay at peak hours reduces by 129.2 and 251.7 s. Accordingly, the road capacity is improved significantly.

Traffic congestion levels change with traffic conditions of different cities. In this paper, we carry out data mining based on traffic flow data obtained by vehicle positioning and video detection. Three basic indicators are selected to measure traffic congestion level, including speed ratio, road link saturation, and intersection saturation. The index weight is determined by expert scoring and hierarchical analysis. A fuzzy evaluation method is developed to calculate the road link congestion level based on the principle of maximum membership degree. The secondary fuzzy evaluation method is applied to evaluate the regional congestion level considering the total driving time of each road link. According to the principle of congestion level division, the traffic congestion level surrounding scenic spots in Yangzhou is studied, and the disturbances of different influencing factors for typical regional traffic congestion are analyzed.

The application of variable message sign (VMS) is an important means to relieve traffic congestion. A bi-level programming model is developed to optimize VMS locations considering stochastic user equilibrium (SUE). The upper level model is a planning model that maximizes the utility and minimizes the cost of VMS. The lower level model is a SUE model that is applied to represent route choice behavior under the guidance of VMS. The solution algorithm integrates the method of successive averages (MSA) and an improved genetic algorithm (GA), which searches for the optimal solution. The road segment of Nan’er Huan Road in Xi’an composes the case study. The results show optimal VMS locations considering SUE on sections 10, 12, 14, and 16. The proposed solution algorithm can effectively optimize the study’s multi-objective VMS location problem. The optimal solution has led the results to outperform the operation based on the current VMS layout.

Based on the theory of shared parking, a shared parking decision in residential areas is designed as a pricing and parking space allocation jointed model. Because the available time gap of parking spaces in residential areas have the characteristics of fragmentation, the sharing mechanism of parking spaces in residential area is considered. The allocation strategies such as first-come-first-served (FCFS) and optimal allocation (OA) are compared. For improving the utilization and economic efficiency of parking facilities, the optimal allocation model of parking space is established and the solution algorithm is designed. Net benefit and parking duration utilization are proposed as evaluation indexes and simulation experiment is carried out. The results show that utilization rate of parking time can increase by 12.27%, and the profit rate can increase by 18.08%, which indicates that after setting the shared parking, over-saturated parking in residential areas reduces significantly, and parking space utilization efficiency improves.

This paper is focused on a method, the route-choice model (RC model) for commuters, which computes the optimal route for a high traffic flow of commuters and optimizes nonlinear coordinated traffic signal timing plans in a square area. This method of designing a turning green wave is based upon the design procedure of a straight green band. Taking traffic data collected in Huilongguan of Changping District in Beijing as an example, it distributes the traffic flow of commuters into several routes respectively and gets the reports of network performance using VISSIM simulation model. The optimal route is chosen based on comparative analysis of the methods of effectiveness (MOEs), such as average delay, bandwidth efficiency, etc. The simulation results show that the proposed scheme is consistent with the RC model, which verifies the effectiveness of the RC model for the grid road network.

This paper aims to analyze the threshold of saturated flow of each lane in an eight-lane expressway merging area under different situations. The concept of saturated is defined firstly and the three-dimensional fundamental diagram which determines the flow, speed, and density thresholds for each lane in the merging area is plotted. Meanwhile, a cellular automaton model is established and validated considering the actual measured data. Results show that the cellular automaton model is effective and the impact of length of acceleration lane, randomization probability, percentage of large vehicles, and number of lanes would change the saturated flow threshold of each lane respectively.

Based on the most widely used ALINEA (asservissement linéaire d’entrée autoroutière) algorithm in the ramp metering field, this paper optimizes the algorithm by using a wavelet neural network algorithm to predict traffic flow and consider the problem of inflow reduction in multi-lane traffic. For the multi-ramp cooperative control problem, the redundancy time is taken as the main index. The classifier’s design is based on pattern recognition theory. The important nodes are identified by K-means algorithm. The improved ALINEA algorithm is used to implement ramp control for important nodes. By controlling the key nodes of the expressway, the traffic efficiency of the expressway system is improved.

In order to increase the capacity and mitigate the traffic congestion at signalized and closely spaced T-intersections, an unconventional closely-spaced T-intersections design (UCTD) was proposed. Through setting back the main T-intersection’s left-turn conflict point to the adjacent T-intersection, there is no need to change the road’s land use or set additional pre-signals for the crossover segment. A two-phase scheme can be used in the main T-intersection for better performance of the heavy traffic flow. Micro simulation was developed to evaluate the UCTD’s performance. In one scenario, the UCTD resulted in a reduction of average delays by 64%, stops by 46%, average stop time by 72%, average queue length by 50%, maximum queue length by 55%, and fuel consumption and related emissions by 14% compared to conventional T-intersections. Also, results found that the UCTD is suitable when the main T-intersection’s traffic volume is much larger than that of the second one.

Commuter flow is an important part of metro passenger flow; accurately identifying commuter flow, finding the current distribution characteristics and changing rules of passenger flow are of great significance to rail transit planning, daily driving organization, and operation management. In order to effectively identify commuter flow, this paper proposes a method for screening and identifying metro card data based on the screening set. Metro card data includes IC card, mobile payment, and other types of data; this paper analyzes all types of card data, summarizes the characteristics of commuter flow, and proposes a simple and reliable screening rule to screen and identify commuter flow data. Finally, this paper conducts an example study of commuter flow based on the card data of Hangzhou Metro Line 2, and the results show that the proposed method meets the actual requirements.

The estimation of queue length at a signalized intersection, while enormously valuable in traffic operation and management, presents a number of challenges to transportation. One area where traffic optimization and analysis is lacking is the ability to dynamically estimate queue length at intersections where only limited GPS trajectory data were recorded and saved. To solve this challenge, an integer-programming model was proposed to estimate queue length and guarantee the consistent reconstruction of shockwave propagation. The model was further evaluated by comparing the estimated queue length with observed queue length in every signal period based on simulation data. The evaluation results not only demonstrate that the proposed model can estimate queue length, but also indicates the required penetration rate of floating vehicles.

Non-green channel vehicles affect the normal operation and management of highways. This study investigates the different characteristics between green and non-green channel vehicles. The binary Logistic regression model was developed to screen suitable variables and identify the non-green channel vehicles rapidly and accurately. Data were collected from Liwen expressway in Jiangxi Province. The result indicated that the weight of vehicle and goods, the vehicles provinces, vehicle types, charge amount, and time information are contributing variables. And based on the model, 97.9% of the non-green channel vehicles were correctly classified, while 92.6% of the green channel vehicles were correctly classified. In addition, the non-green channel vehicles are more likely to pass through in the early morning and evening, while green channel vehicles have no obvious time preference. These findings from the study provide useful insights in detecting non-green channel vehicles rapidly.

At present, there is a lack of research on the speed control of low-grade roads in the urban-rural fringe. It is of great practical significance to improve the safety level of low-grade roads in the urban-rural fringe by carrying out the research on the speed control methods suitable for the roads. This paper proposes a speed control section division scheme based on the sequential cluster method according to the traffic characteristics of low-grade roads in the urban-rural fringe. In studying the spot speed distribution characteristics of low-grade roads in the urban fringe based on the field observation, this paper puts forward the quantitative criteria for the speed-limit section division based on the cluster analysis and the existing specifications with the consideration of vehicle operating coordination.

Advanced strategies for emergency logistics scheduling problem has been a challenging topic for centuries. This study proposed a cluster-first-route-second constructive heuristic method based on the continuous approximation (CA) for disaster relief operation which can turn the relatively complicated vehicle routing problem (VRP) into travel salesman problem (TSP) for pre-assigned customer sets. CA is adopted to determine optimal replenish amount and inventory level for the route serving a given location. Christofide’s method is then employed to solve the TSP for the selected cluster. We investigate two clustering methods, a local-based approach, where clustering and routing are determined, and K-mean clustering where points are clustered upfront by the CA solution. A case study is presented to demonstrate the proposed approach and compare two clustering methods. The numerical results illustrate the effectiveness of the algorithms and conclude that the local-based clustering approach may yield a lower total cost with a higher motion cost.

The coordination efficiency of an urban arterial traffic signal coordination control system is affected by many factors, such as the average speed and traffic flow. The changes of these factors will result in the reduction of coordination efficiency and affect the reliability of the coordination control system. This paper mainly studies the influence degree of average speed and traffic flow on reliability, taking the delay and the number of stops as evaluation indices. Finally, a reliability model of a coordination control system was developed. Different conditions were designed and the data under different conditions was obtained using VISSIM simulation. Based on the simulation data, the influence degree of these factors on delay and the number of stops was analyzed. The calculations of the reliability of the coordination control system under different conditions were found and the findings of this paper will provide a basis for traffic operation management in the future.

Urban traffic is a complex temporal-spatial process. Understanding the dynamical behavior of the whole urban traffic system will allow traffic organizers to identify the source of traffic congestion. In this study, we conducted an in-depth analysis of a taxi trajectory dataset in Beijing based on a dynamical graph and adopted a traffic-modified PageRank algorithm to evaluate urban traffic demands. By generating feature vectors, we have analyzed the temporal-spatial patterns of the distribution of traffic demands in Beijing. We obtained a general picture of the distribution of traffic demands in Beijing and also successfully extracted different zones with significant traffic demands. We discovered that most of Beijing’s traffic demands lie on internal ring roads at daytime and on peripheral highways at nighttime, which suggests that the structure of road network and drivers’ proneness for choosing quicker paths are still the most influential factors of urban traffic.