  Emergency vehicle (EMV) service is a key function of cities and is
exceedingly challenging due to urban traffic congestion. A main reason behind
EMV service delay is the lack of communication and cooperation between vehicles
blocking EMVs. In this paper, we study the improvement of EMV service under V2I
connectivity. We consider the establishment of dynamic queue jump lanes (DQJLs)
based on real-time coordination of connected vehicles. We develop a novel
Markov decision process formulation for the DQJL problem, which explicitly
accounts for the uncertainty of drivers' reaction to approaching EMVs. We
propose a deep neural network-based reinforcement learning algorithm that
efficiently computes the optimal coordination instructions. We also validate
our approach on a micro-simulation testbed using Simulation of Urban Mobility
(SUMO). Validation results show that with our proposed methodology, the
centralized control system saves approximately 15\% EMV passing time than the
benchmark system.
