Different toll collection types of vehicles and different distribution of tollbooths lead to the toll plaza diverging area becoming a typical vehicle weaving area with frequent crossing behaviors and conflicts on highways. This study aims to identify contributing factors to conflict risks of four RP by developing random parameters ordered logit models with heterogeneity in means and variances. The model can flexibly capture the unobserved heterogeneity of the contributing factors in different vehicle-following patterns. Real-world vehicle trajectory data obtained from the toll plaza diverging area in Nanjing, China, are used for model estimation. The results show that vehicle-following patterns with the same toll collection types have a higher percentage of severe conflict risks. The average acceleration of the following vehicles, lane-marking indicator, the initial lanes and lane changes of vehicles are significantly associated with the collision risk levels. The standard deviation of surrogate safety measures of all vehicles in sub-segments are found to differ significantly between vehicle-following patterns. Furthermore, a series of likelihood ratio tests are adopted to test the spatial dependence in sub-segments of the diverging area. The findings of this study could provide valuable information for safety improvement in toll plazas.

The purpose of this study is to explore the factors associated with car use for short trips. Using the data collected from a travel survey conducted with car users in Kunming, the structural equation model is employed to explore the structural relationships between car use for short trips, attitudes toward cars and alternative travel modes, sociodemographics, and the built environment. The results show that instrumental and symbolic attitudes toward cars are positively related to the affective attitude, and these three attitudes have a significant effect on car use for short trips. The symbolic attitude of drivers is negatively associated with their walking and cycling attitudes. Drivers with a better cycling attitude use a car less frequently for short trips. Concurrently, the effects of sociodemographics and the built environment on the attitudes and car use for short trips are also identified. The findings may contribute to understanding the car use behavior and help policy makers to identify methods for reducing car use more clearly.

Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an example, this study proposes a lateral motion strategy for AVs based on deep reinforcement learning (DRL) algorithms. First, a microscopic simulation platform is developed to simulate the realistic diverging trajectories of human-driven vehicles (HVs), providing AVs with a high-fidelity training environment. Next, a DRL-based self-efficient lateral motion strategy for AVs is proposed, with state and reward functions tailored to the environmental features of the diverging area. Simulation results indicate that the strategy can significantly reduce the diverging time of single vehicles. In addition, considering the long-term coexistence of AVs and HVs, the study further explores how the varying penetration of AVs with self-efficient strategy impacts traffic flow in the diverging area. Findings reveal that a moderate increase in AV penetration can improve overall traffic efficiency and safety. But an excessive penetration of AVs with self-efficient strategy leads to intense competition for limited road resources, further deteriorating operational conditions in the diverging area.

Different toll collection types of vehicles and different distribution of tollbooths lead to the toll plaza diverging area becoming a typical vehicle weaving area with frequent crossing behaviors and conflicts on highways. This study aims to identify contributing factors to conflict risks of four vehicle-following patterns in a toll plaza diverging area by developing random parameters ordered logit models with heterogeneity in means and variances. The model can flexibly capture the unobserved heterogeneity of the contributing factors in different vehicle-following patterns. Real-world vehicle trajectory data obtained from the toll plaza diverging area in Nanjing, China, are used for model estimation. The results show that vehicle-following patterns with the same toll collection types have higher percentage of severe conflict risks. The average acceleration of the following vehicles, lane marking indicator, the initial lanes and lane-changes of vehicles are significantly associated with the collision risk levels. The standard deviation of surrogate safety measures of all vehicles in sub-segments are found differ significantly between vehicle-following patterns. Furthermore, a series of likelihood ratio tests are adopted to test the spatial dependence in sub-segments of the diverging area. The findings of this study could provide valuable information for safety improvement in toll plazas.