Traffic injury prevention and road safety are critical public health priorities shaped jointly by transportation engineering, epidemiology, and data science, as traffic crashes continue to cause substantial mortality, disability, socioeconomic costs, and inequities worldwide. Conventional crash records and epidemiological analyses have generated important knowledge about the roles of road design, vehicle characteristics, human behavior, environmental conditions, and post-crash care. However, the growing deployment of intelligent transportation systems (ITS), advanced driver assistance systems (ADAS), automated and connected vehicles, and multimodal sensing technologies is transforming the safety landscape by generating data that functions as both an engineering signal and an epidemiological exposure record. Recent research using naturalistic driving data, high-resolution vehicle trajectories, video analytics, geographic information systems, remote sensing, and connected-vehicle data has improved the identification of crash precursors, high-risk locations, and injury-severity patterns. Machine learning, causal inference, explainable artificial intelligence, Bayesian modelling, digital twins, and real-time risk prediction have become a shared analytical language across these fields, creating opportunities for proactive safety management. Nevertheless, challenges remain in integrating heterogeneous data, validating predictive models across settings, addressing bias and equity, explaining technology-related risks, and translating analytical findings into effective interventions. Further interdisciplinary investigation is needed to establish how emerging data and technologies can support practical, equitable, and public health-oriented approaches to reducing traffic injuries and fatalities.

This Research Topic aims to advance data-driven and evidence-based strategies for preventing traffic crashes, reducing injury severity, and promoting safer transportation systems as a single connected system rather than parallel engineering and health tracks. It will examine how emerging data sources, intelligent transportation technologies, roadway and vehicle design, human factors, and advanced analytical methods can identify risks, explain crash mechanisms, evaluate interventions, and inform policy. Contributions may investigate the safety and public health effects of ADAS, automated and connected vehicles, infrastructure-based countermeasures, and real-time warning systems, as well as the needs of vulnerable road users and populations in rural, urban, and underserved settings. The Research Topic also seeks evidence on how safety technologies and interventions perform across different environmental, socioeconomic, regulatory, and transport contexts, and how their benefits and limitations can be assessed through robust, transparent, and reproducible methods.

To gather further insights into data-driven road safety and traffic injury prevention across rural, urban, connected, automated, and vulnerable-road-user contexts, we welcome original research, reviews, and methodological contributions addressing, but not limited to, the following themes:

Data-driven crash, near-miss, and injury-severity analysis using crash records, naturalistic driving data, trajectory data, connected-vehicle data, video, GIS, and remote sensing

Real-time crash-risk prediction, proactive safety management, and intelligent traffic control

Public health impacts of roadway design, infrastructure quality, land use, built environments, and transportation inequities

Safety evaluation of ADAS, automated vehicles, connected vehicles, vehicle-to-everything communication, and infrastructure-based countermeasures

Machine learning, deep learning, causal inference, explainable AI, Bayesian methods, physics-informed models, generative AI, and digital twins for safety research

Safety of pedestrians, cyclists, motorcyclists, micromobility users, children, older adults, and other vulnerable road users

Road safety challenges and interventions in rural, urban, low-resource, and rapidly developing transportation environments

Human factors, driver behavior, physiological monitoring, human–machine interaction, and technology acceptance

Evidence-based policies, infrastructure interventions, behavioral programs, emergency response, and post-crash care

Climate resilience, extreme weather, cybersecurity, ethics, privacy, and governance in intelligent transportation systems

International comparisons of road safety performance, injury prevention policies, and technology implementation

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Advancing Road Safety through Intelligent Transportation and Public Health Innovation