Modelling green waves for emergency vehicles using connected traffic data

Bieker-Walz, Laura; Behrisch, Michael · 2019 · Crossref

DOI: 10.29007/sj1m

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the critical need for faster and safer routing of emergency vehicles, which face significantly higher crash risks—particularly at signalized intersections—and frequent delays due to traffic congestion. The authors propose a self-organizing "green wave" strategy that prioritizes emergency vehicles at traffic lights while minimizing negative impacts on other traffic participants. The study introduces a new prioritization algorithm called WALABI, which utilizes connected traffic data to dynamically adjust traffic light timing based on real-time conditions. The WALABI approach relies on vehicular communication, where emergency vehicles transmit Cooperative Awareness Messages and route information to roadside units, which forward the data to a Traffic Management Center. The center then coordinates traffic lights along the vehicle’s path. To handle conflicts between multiple emergency vehicles, the algorithm prioritizes based on arrival time and vehicle class (ambulances, fire brigades, police). Crucially, WALABI calculates the optimal distance to switch a traffic light to green by estimating the time required to clear waiting vehicles and the emergency vehicle itself. This calculation incorporates the number of waiting vehicles, a safety buffer, and the emergency vehicle’s speed. The algorithm also accounts for downstream congestion by checking if subsequent intersections have sufficient capacity to absorb the released traffic, adjusting timing accordingly to prevent new jams. The performance of WALABI was evaluated using microscopic traffic simulations in SUMO, comparing it against two existing strategies: FAST (simple green phase extension) and STREAM (GPS-based fixed-distance triggering). Simulations were conducted on simple intersection and multi-intersection corridor scenarios with varying traffic volumes and lane configurations. Results indicated that while FAST performed slightly better in low-density single-intersection scenarios, it risked failing if vehicles exceeded expected passage times. In contrast, WALABI and STREAM showed similar average travel times in simple scenarios. However, WALABI demonstrated superior performance in high-density traffic and corridor scenarios. By dynamically adapting to traffic queues and coordinating multiple intersections, WALABI reduced emergency vehicle travel times more effectively than STREAM, particularly when congestion was severe. The study concludes that WALABI offers the best average travel time results for emergency vehicles, especially in complex, high-density environments where static algorithms like STREAM struggle. The findings highlight that while prioritization significantly aids emergency response, extreme traffic density can still cause delays. The authors suggest future work should involve real-world case studies to further define the algorithm’s limitations and validate its effectiveness in practical urban settings.

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• emergency work zone conspicuity