A dynamic game model based on multiple decision makers is developed to minimize pedestrian–vehicle conflicts at unsignalized crosswalks. The characteristics of pedestrians and drivers crossing the street are divided into traffic features, behavioral characteristics, and psychological factors, using data collected from a survey of pedestrian crossing behavior. Based on waiting delay and risk assessment, a utility model of pedestrian crossing choice behavior is presented, and a probability model for different phases of the waiting stage is developed by splitting the waiting process into three stages with pedestrian waiting duration. The payment function and the game model of pedestrian–vehicle conflict based on distinct previous decision makers are developed using game theory and “mixed strategy”, and they are resolved in equilibrium.

Understanding the causes of traffic road accidents is crucial; however, as data collection is conducted by traffic police, accident-related environmental information is not available. To fill this gap, we collect information on the built environment within R = 500 m of the accident site; model the factors influencing accident severity in Shenyang, China, from 2018 to 2020 using the Random Forest algorithm; and use the SHapley Additive exPlanation method to interpret the underlying driving forces. We initially integrate five indicators of the built environment with 18 characteristics, including human and vehicle at-fault characters, infrastructure, time, climate, and land use attributes. Our results show that road type, urban/rural, season, and speed limit in the first 10 factors have a significant positive effect on accident severity; density of commercial-POI in the first 10 factors has a significant negative effect. Factors such as urban/rural and road type, commercial and vehicle type, road type, and season have significant effects on accident severity through an interactive mechanism. These findings provide important information for improving road safety.

In this study, factors affecting crash duration and geostatistical analysis were examined using traffic crash data from Houston, USA. Significant factors affecting road crash duration included 14 independent factors related to time, roadway, and environment. Delays caused by traffic crashes were used as an indicator to assess the impact of traffic crashes of different severity on the roadway network. In addition, the spatial distribution characteristics of the different values corresponding to each key impact factor under different scenarios in the region were studied using ArcGIS kernel density analysis techniques. This is because the combination of these two methods is more operational and understandable. The results show that accidents are more likely to occur at night and on holidays, that accidents last longer after they occur in residential areas, and that accident duration varies near different road facilities. This study may provide a reference for targeted management and improvement measures for road safety.