This paper proposes a biobjective continuous transportation network design problem concerning road congestion charging with the consideration of speed limit. The efficiency of the traffic network and the reduction of pollution in the network environment are improved by designing a reasonable road capacity enhancement and speed limit strategy. A biobjective bilevel programming model is developed to formulate the proposed network design problem. The first target of the upper problem is the optimization of road charging efficiency, and the other target is the total cost of vehicle emissions; these objectives are required to devise the optimal road capacity enhancement scheme, speed limiting schemes for different time periods, and the road pricing scheme. The lower-level problem involving travellers’ route choice behaviours uses stochastic user equilibrium (SUE) theory. Based on the nondominated sorting genetic algorithm, which is applied to solve the bilevel programming model, a numerical example is developed to illustrate the effectiveness of the proposed model and algorithm. The results show that the implementation of congestion charging measures on the congested road sections would help to alleviate road congestion in the transportation network, effectively save transportation infrastructure investment and limited urban land resources, increase fiscal revenue, and open up new sources of funds for urban infrastructure construction.

Advanced cooperative vehicle fleet technology has shown potential for improving safety and efficiency near freeway bottlenecks. This study focuses on resolution strategies for cooperative vehicle fleets for reducing rear-end collision risks near recurrent bottlenecks. Based on different vehicle types and time requirements and intentions, different resolution strategies are proposed to determine the travel rules and sequence of vehicle flows when a cooperative vehicle fleet needs to change its original fixed formation. Then, a series of experimental scenarios is designed by setting different vehicle types (conventional vehicles and connected and automated vehicles), requirements and intentions in different areas of multilane freeways. The experimental scenarios are simulated based on the MATLAB platform to test the proposed strategy. The results show that the proposed strategy can effectively reduce vehicle rear-end collisions, and the average cost savings of the conflict resolution process is 25.71%. The proposed strategies in this research provide autonomous resolutions for vehicle conflicts in different scenarios.

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT.

Understanding the spatiotemporal heterogeneity of shared mobility is essential for the development of sustainable transportation systems. However, there is a notable lack of comprehensive research examining the differences in spatiotemporal heterogeneity between ride-hailing and bike-sharing ridership. This study seeks to fill this gap by analyzing order data to investigate the patterns of ridership for both ride-hailing and bike-sharing services. By applying Geographically and Temporally Weighted Regression and the Herfindahl-Hirschman Index, the study uncovers the characteristics of spatiotemporal distribution and the factors influencing these patterns. The findings indicate that road network density, transit accessibility, and commercial land use have a significant impact on both ride-hailing and bike-sharing ridership. Additionally, the analysis reveals that bike-sharing ridership exhibits strong tidal and spatial aggregation effects, whereas ride-hailing demand shows considerable spatial heterogeneity. The insights derived from this study are crucial for policy-making and decision-making processes aimed at promoting shared mobility systems.