With emergency vehicle response times increasing due to increasing congestion, there needs to be more technological solutions beyond using only sirens. We show how using reinforcement learning for route guidance integrated with signal control can reduce EMV response time by up to 43% in simulated scenarios over existing technologies. Should be of interest for discussion between traffic agencies (New York City Department of Transportation) and EMVs (New York City Fire Department, NYU Langone Health). Part of Haoran Su’s dissertation and in collaboration with Siemens. Funding from Siemens, Dwight David Eisenhower Transportation Fellowship, C2SMART University Transportation Center, NSFC Project 62103260, SJTU UM Joint Institute, J. Wu & J. Sun Endowment Fund, and the NYU University Research Challenge Fund 2020.

Paper can be found here: https://www.sciencedirect.com/science/article/pii/S0968090X22003680