This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

This study aims to understand how attitudinal factors influence individuals’ choices for further developing carsharing. To address the gap, a household survey was carried out to obtain the individual’s attitudes toward carsharing. The Transtheoretical Model is used to divide the adoption process of carsharing into five stages, including precontemplation, contemplation, preparation, action, and maintenance. Two Integrated Choice and Latent Variable Models with or without the attitudinal features are evaluated for the effects of latent attitudes on the above five stages. Additionally, the attitudinal factors at each stage are analyzed and compared, and the effects of the flexible and hard measures on the adoption process are discussed. The results indicate that the proposed attitudinal factors well reflect individuals’ adoption stage of carsharing and show different effects on the five adoption stages. The policy implications for facilitating carsharing require coordinating flexible and hard measures in different cognitive stages.

Shared bicycle users with commuting purposes generally need to take a traditional public transit and then use the shared bicycle to complete the first/last mile transport. While shared bicycle provides convenient travel for travelers, it also brings a series of problems such as disorderly parking and road occupancy. Therefore, exploring the problem of travel mode shift between shared bicycle and public transit is of significance for improving the traffic environment and increasing the sharing rate of public transit. This paper introduces the flex-route transit system and quantitatively analyzes the rationality and feasibility of using flex-route transit to pick up and drop off shared bicycle users with commuting demand from the temporal perspectives. A flex-route transit route design model is established with the objective of minimizing the sum of vehicle driving time cost and passenger time cost, and the time cost models of the shared bicycle commuting system and the flex-route transit system are constructed, compared, and analyzed to explore the feasibility of flex-route transit picking up or dropping off shared bicycle users under different conditions. Through the subsequent sensitivity analysis, the influence of passenger demand density, fixed station spacing, and travel preference attributed to the two systems are analyzed separately. The results demonstrate that the flex-route transit can efficiently complete the picking up or dropping off for shared bicycle users under certain conditions.

Travel survey data has long been one of the fundamental sources for travel mode choice behavior analysis. It can provide a wealth of travel-related information and detailed household attributes of individuals. However, precise travel path data, especially data relating to choices is usually lacking. Online Amap API service is promising to compensate for this lack by providing tools to calculate routes, and display key information such as travel time and cost. This study integrated revealed choice survey data with path data from an online Amap API, creating a multi-source combined dataset. Furthermore, two machine learning models (extreme gradient boosting and random forest) were applied for travel mode predictive analysis based on the original travel survey data and the integrated data. Results exhibit that the overall prediction accuracy of the XGB model improves from 0.82 to 0.84, the RF model from 0.78 to 0.82, demonstrating that integrating Amap API data can improve the performance of models is improved after Amap API data is integrated. Additionally, the Shapley additive explanations analysis (SHAP) method was applied for model interpretation. The results identify travel cost, travel time, age, and monthly income as the key factors. These findings highlight the importance of fusing big data techniques with small data in enhancing predictive accuracy and provide valuable insights into travel behavior studies.

The bilateral Bus Rapid Transit (BRT) system is a kind of BRT system in which the stops are located in the middle of the transit lane. By simultaneously serving transit lines in opposite directions, it is particularly designed to save space resources and enhance service quality. To improve the operational efficiency of the bilateral BRT, this paper optimizes the operational performance of bilateral BRT with elastic demand. The objective is to minimize the generalized time cost per passenger of the system by jointly optimizing the headway and number of stops of bilateral BRT. The cost includes the agency operating and user travel. The optimal design model is formulated as a mixed-integer program and solved using a fuzzy analytic hierarchy process (FAHP) and a genetic algorithm (GA). We conduct a case study and sensitivity analysis to show the effectiveness and reliability of the proposed approach. We conclude that the optimized minimum generalized cost per passenger is lower than the actual case for all demand levels, especially at off-peak hours, by about 22.5%. In addition, we find that the weights of agency and user costs have the most significant impact on headway, whereas the influence of walking, vehicle speed, and route length is minimal. In contrast, the optimal number of BRT stops is mostly influenced by the route length, and walking speed has essentially no effect on the optimal number of stops. Finally, we find that the generalized cost per passenger at peak hours is 10% to 15% smaller than at off-peak hours in various scenarios.