RFAs: Predicting and avoiding road crashes based on Artificial Intelligence and big data

Project results are expected to contribute to all the following expected outcomes: knowledge of high-risk locations along the road network becoming available, before crashes actually occur, enabling road authorities to deploy appropriate countermeasures proactively; predictive identification of safety-critical situations based on data from multiple sources and enabling real-time interventions to avoid crashes; determination of the optimal sample size to allow for reliable real-time crash occurrence prediction; enhanced monitoring of traffic flows and incorporation of traffic flow variations and patterns in real-time crash prediction, which will also lead to more effective traffic management by foreseeing unexpected or disruptive events.

The scope of this call is rooted in the Safe System Approach, which emphasizes shifting from reactive to proactive management of road safety by intervening before serious crashes occur. The growing availability of diverse data and the capacity to process it within digital transport systems support this vision. Data sources may include smartphones, wearables, connected vehicles, drones, and roadside sensors such as cameras and radars. Advances in computing power, location services, and video analytics enable the detection of safety-critical situations based on surrogate safety metrics.

Artificial intelligence holds strong potential in this domain, as it can uncover complex relationships within large datasets, identify crash-contributing factors, and predict safety-critical situations at quantifiable risk levels. This predictive capability allows for the proactive implementation of avoidance measures. Proposals under this call are expected to develop AI-enabled digital twins of traffic and infrastructure, integrating historical, current, and forecast data from various sources, including environmental and road conditions.

The topic is classified as a Research and Innovation Action with an indicative budget of EUR 10.00 million, expecting EU contributions of around EUR 5.00 million per project, funding approximately two projects. Activities are expected to reach Technology Readiness Level 5-6 by the end of the project.

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