The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate interaction between these entities within complex driving environments. AVs are expected to have human-like driving behavior to seamlessly integrate into human-dominated traffic systems. To address this issue, we propose a reinforcement learning framework that considers driving priors and Social Coordination Awareness (SCA) to optimize the behavior of AVs. The framework integrates a driving prior learning (DPL) model based on a variational autoencoder to infer the driver's driving priors from human drivers' trajectories. A policy network based on a multi-head attention mechanism is designed to effectively capture the interactive dependencies between AVs and other traffic participants to improve decision-making quality. The introduction of SCA into the autonomous driving decision-making system, and the use of Coordination Tendency (CT) to quantify the willingness of AVs to coordinate the traffic system is explored. The unsignalized intersection serves as a representative experimental scenario. Simulation results show that the proposed framework can not only improve the decision-making quality of AVs but also motivate them to produce social behaviors, with potential benefits for the safety and traffic efficiency of the entire transportation system.

Objective: Rear-end accidents are the most common accident type at signalized intersections, because the diversity of actions taken increases due to signal change. Green signal countdown devices (GSCDs), which have been widely installed in Asia, are thought to have the potential of improving capacity and reducing accidents, but some negative effects on intersection safety have been observed in practice; for example, an increase in rear-end accidents. Methods: A microscopic modeling approach was applied to estimate rear-end accident probability during the phase transition interval in the study. The rear-end accident probability is determined by the following probabilities: (1) a leading vehicle makes a “stop” decision, which was formulated by using a binary logistic model, and (2) the following vehicle fails to stop in the available stopping distance, which is closely related to the critical deceleration used by the leading vehicle. Based on the field observation carried out at 2 GSCD intersections and 2 NGSCD intersections (i.e., intersections without GSCD devices) along an arterial in Suzhou, the rear-end probabilities at GSCD and NGSCD intersections were calculated using Monte Carlo simulation. Results: The results suggested that, on the one hand, GSCDs caused significantly negative safety effects during the flashing green interval, especially for vehicles in a zone ranging from 15 to 70 m; on the other hand, GSCD devices were helpful in reducing rear-end accidents during the yellow interval, especially in a zone from 0 to 50 m. Conclusions: GSCDs helped shorten indecision zones and reduce rear-end collisions near the stop line during the yellow interval, but they easily resulted in risky car following behavior and much higher rear-end collision probabilities at indecision zones during both flashing green and yellow intervals. GSCDs are recommended to be cautiously installed and education on safe driving behavior should be available.