This study quantifies the impact of individual attributes, the built environment, and travel characteristics on the use of bike-sharing and the willingness of shifting to bike-sharing-related travel modes (bike-sharing combined with other public transportation modes such as bus and subway) under different scenarios. The data are from an RP (Revealed Preference) survey and SP (Stated Preference) survey in Nanjing, China. Three mixed logit models are established: an individual attribute–travel characteristics model, a various-factor bike-sharing usage frequency model, and a mixed scenario–transfer willingness model. It is found that age and income are negatively associated with bike-sharing usage; the transfer distance (about 1 km), owning no car, students, and enterprises are positively associated with bike-sharing usage; both weather and travel distance have a significant negative impact on mode shifting. The sesearch conclusions can provide a reference for the formulation of urban transportation policies, the daily operation scheduling, and service optimization of bike-sharing.

The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles’ motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TD3) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories.

Energy-efficient driving systems can effectively reduce energy consumption during vehicle operation. Most of the existing studies focus on the driving strategies in a fixed signal timing environment, whereas the standardized Signal Phase and Timing (SPaT) data can help the vehicle make the optimal decisions. However, with the development of artificial intelligence and communication techniques, the conventional fixed timing methods are gradually replaced by adaptive traffic signal control (ATSC) approaches. The previous studies utilized SPaT information that cannot be applied directly in the environment with ATSC. Thus, a framework is proposed to implement energy-efficient driving in the ATSC environment, while the ATSC is realized by the value-based reinforcement learning algorithm. After giving the optimal control model, the framework draws upon the Markov Decision Process (MDP) to make an approximation to the optimal control problem. The state sharing mechanism allows the vehicle to obtain the state information of the traffic signal agents. The reward function in MDP considers energy consumption, traffic mobility, and driving comfort. With the support of traffic simulation software SUMO, the vehicle agent is trained by Proximal Policy Optimization (PPO) algorithm, which enables the vehicle to select actions from continuous action space. The simulation results show that the energy consumption of the controlled vehicle can be reduced by 31.73%~45.90% with a different extent of mobility sacrifice compared with the manual driving model. Besides, we developed a module based on SHapley Additive exPlanations (SHAP) to explain the decision process in each timestep of the vehicle. That can make the strategy more reliable and credible.

Massive short-term passenger flow prediction models of urban rail transit stations have been used in different conditions. However, researchers encountered several challenges while selecting the optimal passenger flow information input matrix and eliminating the redundant information in the original data. In this paper, we propose a learning network based on the optimal passenger flow input information algorithm (MTFLN) method. Based on the passenger flow information attribute of the predicted target station and the correlation coefficient distribution characteristics in different stages, the parameters of the optimal passenger flow information input algorithm (OPFIIA) are set reasonably. The optimal passenger flow input information matrix is also determined. We combine the training results of MTFLN method to optimize parameters of the Adam optimization algorithm-long and short-time memory network (Adam-LSTM). In the case study, the short-term passenger flow predicted values of urban rail transit stations are presented by three predicted methods including Adam-LSTM, Elman neural network and autoregressive integrated moving average (ARIMA). The presented results demonstrate that selecting the initial time correlation matrix by combining the attributes of the predicted target station and using OPFIIA method can not only effectively select the optimal passenger flow input information matrix, but also improve training efficiency and the prediction accuracy of traditional predicted model.