Intelligent Transportation System

Warning information produced by today’s vehicles has quietly grown into one of the most valuable data sources in Intelligent Transportation Systems (ITS). Instead of waiting for crashes to occur and then analyzing what went wrong, transportation agencies and researchers are increasingly turning toward warnings that reflect risky moments as they happen. With systems like ADAS, DMS, BSD, and modern telematics becoming standard equipment, vehicles now emit a steady flow of alerts that reveal driver behavior, surroundings, and roadway conditions with surprising detail. These warnings—sometimes subtle and sometimes urgent—offer a clearer, more immediate picture of safety performance than traditional crash databases, which are often sparse or outdated by the time they are analyzed. This review takes a broad look at how such warning data is gathered, cleaned, interpreted, and eventually transformed into risk assessment insights. We highlight recent work, including a 2025 study on freight vehicles that found a strong spatial overlap between clusters of warning activity and known accident hotspots. Along the way, we examine analytical tools ranging from entropy-based weighting and spatial clustering to emerging machine-learning approaches that identify near-miss patterns. We also consider where the field seems to be heading—multi- sensor fusion, predictive modeling, and increasingly connected traffic ecosystems. Overall, the goal is to show how warning data, when handled carefully, can guide future ITS safety strategies in a much more proactive and adaptive way.