Data-Driven Approaches for Traffic Safety and Road Risk Management

Dear Colleagues,

Transportation safety is a fundamental component of sustainable, resilient, and efficient mobility systems. With the rapid development of sensing technologies, connected and automated vehicles, intelligent infrastructure, and large-scale mobility datasets, traffic safety research is increasingly shifting from traditional, passive statistical analysis toward data-driven, predictive, and real-time approaches. Advanced machine/deep learning, artificial intelligence, and data fusion techniques now enable crash risk prediction at fine spatial and temporal resolutions, such as road segments, intersections, and individual traffic conflicts, while also supporting real-time visualization, interpretation, and management of road safety risks through online platforms and digital twin environments.

This Special Issue welcomes original research articles and review papers focusing on data-driven methodologies for traffic safety analysis, crash prevention, and road risk management. We particularly encourage studies that integrate emerging data sources, advanced analytical methods, and practical safety applications to support proactive and interpretable decision-making for transportation agencies, infrastructure operators, and mobility service providers.

Topics of interest include, but are not limited to, the following:

• Crash risk prediction using machine/deep learning, AI, and hybrid modeling methods;
• Applications of emerging data sources, including connected vehicle data, automated vehicle testing data, automated traffic signal data, vehicle trajectory data, video analytics, and crowdsourced mobility data;
• Spatio-temporal safety analysis, network-level risk mapping, and real-time road risk visualization;
• Explainable AI, causal inference, and interpretable machine learning for traffic safety and risk management;
• Real-time crash risk prediction, proactive safety warning, V2X-based safety applications, and online safety management systems;
• Surrogate safety measures and conflict-based safety analysis using trajectory, video, simulation, or connected vehicle data
• Microscopic traffic simulation, driving simulators, co-simulation, and digital twin applications for safety evaluation;
• Multi-agent traffic safety modeling involving vehicles, drivers, pedestrians, cyclists, and other vulnerable road users;
• Data-driven studies on travel behavior, non-motorized road users, emerging mobility technologies, and their safety implications;
• Fairness, privacy, ethics, and generalizability issues in data-driven traffic safety research. 

Dr. Jinghui Yuan
Dr. Lei Han
Prof. Dr. Xiaobao Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Transportation is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• traffic safety
• road risk management
• crash risk prediction
• traffic conflict analysis
• machine learning (ML) and deep learning (DL)
• artificial intelligence (AI)
• multi-data fusion
• connected and automated vehicle (CAV)
• spatio-temporal safety analysis
• digital twin safety application