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This study selected the Yangtze River Delta urban agglomeration as the research area, considering the current status of its transportation development to construct an urban agglomeration land transportation integration index system and to evaluate the development of the area's transportation integration. The study examined the influence mechanism of transportation infrastructure on service industry agglomeration. The results were as follows: (1) From 2011 to 2020, the Yangtze River Delta urban agglomeration land transportation integration index showed a clear upward trend. (2) The development of the integration level of local transportation has a certain inhibitory effect on the agglomeration of local service industries. The transportation integration of the Yangtze River Delta urban agglomeration played an important role in promoting the agglomeration of local wholesale and retail industries and transportation, storage, and postal services. The labor force and market size also have a significant impact on service agglomeration. (3) The integration of land transport in the urban agglomeration affects the agglomeration of service industries through the knowledge spillover effect caused by the improvement of accessibility, and the impact is heterogeneous. The Yangtze River Delta urban agglomeration needs to accelerate the construction of trans-provincial and trans-municipal transportation infrastructure and to further improve its connectivity level to promote the integrated development of high-quality transportation in this area. [ABSTRACT FROM AUTHOR]

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network., 1. Introduction Nowadays, high traffic demand and limited road capacities make people spend much more time on their daily journeys. Travel time reliability (TTR), defined as the level of consistency [...]

This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development status of the Yangtze River Delta urban agglomeration transportation integration. The study examined the influence mechanism of transportation infrastructure on industrial agglomeration. The results are as follows: (1) From 2011–2020, the Yangtze River Delta urban agglomeration's transportation integration index showed a clear upward trend. (2) The integration level of local transportation played an important role in promoting local industrial agglomeration. Promoting industrial agglomeration in neighboring areas had a negative spillover effect on industrial agglomeration in this region. Developing transportation integration in other regions had an insignificant positive effect on the development of local industrial agglomeration. (3) Urban agglomeration transportation integration impacted regional industrial agglomeration, mainly through the "cost effect." Thus, cities in the Yangtze River Delta in 2020 need to accelerate the construction of relevant transportation infrastructure so as to promote the integrated development of higher-quality transportation in the Yangtze River Delta urban agglomeration. [ABSTRACT FROM AUTHOR]

Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation studies and can be applied to automated driving systems to provide human-like decision making. Previous modeling methods can be mainly divided into scenario-based modeling methods and field theory-based modeling methods. Scenario-based models are based on behavior theories that can explain behavioral mechanisms and field theory-based models are convenient for application to different scenarios. Combining two behavior theories and field theory, this paper aims to present a novel method to uniformly model the driving behavior in different scenarios. Risk homeostasis theory and preview-follower theory are used as the theoretical foundation, and field theory is utilized to connect the two behavior theories. A new risk field model is constructed for better coupling these behavior theories. Integrating these theories, this study then develops a subjectively perceived risk quantification method and a trajectory and motion planning model, which are validated using naturalistic data in car-following scenarios. Results show the effectiveness of this method and this model with reference to an effective risk quantification index (safety margin) and in comparison with the classical models (desired safety margin model and intelligent driver model) using naturalistic data in car-following scenarios. [ABSTRACT FROM AUTHOR]

In recent years, the adaptive cruise control (ACC) system has become widely adopted. The vehicle-positioning system plays an important role in automotive platoon driving and can provide vehicle-position information through vehicle-to-vehicle communication. However, the acquired vehicle-position information often presents uncertainties given the existence of variations in the traffic environment and of noise in the vehicle-positioning system. These uncertainties, in turn, influence the car-following performance of a vehicle platoon. In this study, we employ the desired safety margin (DSM) model, an ACC control strategy, to investigate the influence of the uncertainty level of vehicle-position information (ULVPI) on string stability and car-following safety. The stability criterion of the DSM model with ULVPI is derived through linear stability theory. Theoretical analysis results are verified by numerical simulations. Analytical results indicate that a negative ULVPI value can expand the stable region and improve string stability and that a positive ULVPI value can reduce delay time. Moreover, a negative ULVPI value can improve car-following safety during the stopping and evolution processes, whereas a positive ULVPI values can increase the safety margins of vehicles during the starting process. In the car-following process, a negative ULVPI value can improve car-following safety and reduce rear-end collision risk. The variation in ULVPI values can improve string stability and reduce the risk of rear-end collisions when mean and standard deviation are reasonable. Therefore, the improvement of a vehicle’s dynamic performance, string stability, and safety relies on the effect of ULVPI in different traffic scenarios. These results are useful in designing different control strategies that stabilize traffic flow and improve traffic safety for vehicles with ACC systems. [ABSTRACT FROM AUTHOR]

Drivers’ risk perception is vital to driving behavior and traffic safety. In the dynamic interaction of a driver-vehicle-environment system, drivers’ risk perception changes dynamically. This study focused on drivers’ risk perception at unsignalized intersections in China and analyzed drivers’ crossing behavior. Based on cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established for the crossing processes between two straight-moving vehicles from the orthogonal direction. The acceptable risk perception levels of drivers were identified using a self-developed data analysis method. Based on game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed. The models of drivers’ crossing behavior were then established. Finally, the behavior models were validated using data collected from real-world vehicle movements and driver decisions. The results showed that the developed behavior models had both high accuracy and good applicability. This study would provide theoretical and algorithmic references for the microscopic simulation and active safety control system of vehicles.