Transportation safety is of increasing societal concern. With the introduction of multimodal transportation modes and emerging in-vehicle safety technology systems, traffic safety risk evaluation, prevention, and control have become more challenging. The major challenge comes when integrating multisource data inputs related to traffic safety, and when identifying traffic safety risks associated with new transportation modes such as bike-sharing and online car-hailing.

Specifically, there is great need for developing new methods to integrate multisource transportation data for developing quantitative measurements of traffic safety risk under various driving/riding scenarios, for evaluating the effectiveness of advanced emergency management and traffic resource allocation technology, and for identifying the safety issues associated with the emerging connected and automated vehicles (CAV) technologies. As a result, current research needs are targeting these areas to explore solutions and develop effective methods to address the related challenges.

This Special Issue should present theoretical innovation and provide engineering application value. Original research and review articles are encouraged to focus on multisource data fusion, risky driving behavior modeling, safety risk analysis and quantitative evaluation of new traffic modes (include shared bikes, online car-hailing), traffic safety risk simulation and dynamic scenario analysis, and emergency rescue and traffic resource allocation. We also welcome original research outcomes and review articles involving the GIS-based and CAV-related traffic safety studies that clarify the current state of the art.

Potential topics include but are not limited to the following:

• Emerging methodologies in multisource data fusion and traffic safety risk perception
• Traffic crash risk assessment and prevention in multiple dangerous areas
• Geographic Information System (GIS) methods used in traffic safety risk analysis and identification
• Safety risk analysis and management for new traffic modes (including shared bikes and online car-hailing)
• Risky driving behaviors analysis and modeling under different scenarios such as the regular traffic scenario and CAV scenario
• Investigation of emerging technologies for traffic safety in the context of CAV
• Advanced data analysis methods related to potential traffic crash risk prediction and identification (including risk spread, transfer, and cluster)
• Application of advanced simulation technology in traffic safety analysis, risk prevention, and control
• Innovative methodologies in traffic emergency management and resource allocation