As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.

Predicting the crowd flow in various areas of the city is of strategic importance for traffic control and public safety. In recent years, crowd flow prediction based on spatio-temporal data are gaining more and more attention. In order to better understand the current status of spatio-temporal crowd flow prediction research and global cooperation, we use scientometric methods, social network analysis, and Stochastic Actor-oriented Model (SAOM) to visualize and analyze the source journals, hotspot co-occurrence networks, and national cooperation networks based on the relevant literature included in the Web of Science database, so as to explore the current status and characteristics of related academic research. In addition, this paper constructs a technical framework based on Subject–Action–Object (SAO) structural information, and draws a technical roadmap containing five levels: data, technology, influence factors, objectives, and applications. The visual mapping of the evolutionary path of technology topics based on SAO structure information can assist in analyzing the evolutionary path of technology topics and their development trends. This study contributes to the existing knowledge system of spatio-temporal crowd flow prediction by proposing a new, integrated, and holistic knowledge map.

The job satisfaction level (JSL) of self-employed container truck drivers (SCTDs) is vital to the container trucking industry’s (CTI) stability in China. An anonymous field survey of 645 SCTDs was conducted at Shanghai Port. Three ordered probit models were, respectively, developed to analyze the drivers’ JSLs, their attitudes, and reactions to a CTI downturn. This study contributes to the literature that the demographic variables were not statistically significant for the SCTDs’ current JSLs, while higher income SCTDs with fewer expenses were expected to have higher JSLs. During an industry downturn, drivers with families in Shanghai showed more job dissatisfaction. More working hours, lower income, expensive diesel fuel, a high consumer price index (CPI), and a low freight-to-distance ratio (FDR) significantly lowered the JSLs. An increase in the price of diesel fuel and the CPI, and a lower FDR exposed the industry to risk and instability. Additionally, credible evidence indicated that as the dissatisfaction levels of SCTDs rise in a downturn, SCTDs implement more practical measures that may negatively affect the industry’s stability as well as society’s. Based on these findings, managerial or policy implications were proposed to efficiently improve SCTDs’ JSLs and CTI stability in Shanghai.

The safety situation of hazardous materials (hazmat) accidents during road transportation in China is severe and very serious accidents occurred frequently. Such accidents not only have a huge impact on the environment but also have serious consequences for people and the economy, such as fires and explosions. Therefore, it is necessary to understand the characteristics and laws of road transport accidents of hazmat systematically. This paper investigated 2777 hazmat transportation accidents in China from 2013 to 2019 to identify the characteristics, consequences, and causes of the accident. The results show that August (10.05%) and December (9.76%) are the peak periods of hazmat transportation accidents, while most hazmat transportation accidents occurred in the early morning (6:00–9:00 a.m.) and at noon (9:00 a.m.–12:00 p.m.) hours. For the geographical location, the accidents mainly occurred in the east China (34.35%) and the northwest China areas (14.87%). The main types of hazmat transportation accidents were rollover (35.36%), rear-end (22.58%), and collision (14.87%), where the probability of a major leak was high. The most common hazmat transportation accidents involve gas (17.79%), flammable liquid (56.07%), and corrosive substance (12.28%). The most common consequences of the hazmat transportation accidents were leakage (80.34%), followed by fire release (8.32%) and explosion release (2.34%). Human factor (26.74%) is the main cause of hazmat transportation accidents. These findings could help hazmat transportation managers and planners develop appropriate measures for improving hazmat transportation safety.