An Evaluation of Emergency Vehicle Preferential Treatment Strategies by Traffic Simulation

Emergency vehicles (EVs) must reach their destination within the shortest amount of time considering the importance of their service in saving lives and reducing property damage. This research aimed to reduce the delay experienced by these vehicles through the alteration of traffic control signals that would give preferential treatment to EVs upon detection. Specifically, this study compared three algorithms with concepts of emergency vehicle pre-emption (EVP), longest queue first, and emergency vehicle signal priority (EVSP) based on average general and EV delays through traffic simulation approach via VISSIM and its component object model (COM) interface. The performance evaluation was applied in Tuk Chai Intersection in Bangkok, Thailand as a case study. It was found that the second algorithm of longest queue first with pre-emption is the best approach for both 0.8 (peak) and peak hour traffic conditions, performing better than the do-nothing condition by 69.70% for stochastic EV input and 79.14% for fixed EV input. The findings of the study also show that the algorithm of preferential treatment using EVSP strategies produced a competitive and robust performance that has EV delay values being better than the do-nothing condition by 75.96%, 77.27%, and 45.67% in 0.8 (peak), peak-hour, and 1.2 (peak) traffic conditions, respectively.