  The ever-increasing heavy traffic congestion potentially impedes the
accessibility of emergency vehicles (EVs), resulting in detrimental impacts on
critical services and even safety of people's lives. Hence, it is significant
to propose an efficient scheduling approach to help EVs arrive faster. Existing
vehicle-centric scheduling approaches aim to recommend the optimal paths for
EVs based on the current traffic status while the road-centric scheduling
approaches aim to improve the traffic condition and assign a higher priority
for EVs to pass an intersection. With the intuition that real-time vehicle-road
information interaction and strategy coordination can bring more benefits, we
propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach
including a real-time route planning module and a collaborative traffic signal
control module, which interact with each other and make decisions iteratively.
The real-time route planning module adapts the artificial potential field
method to address the real-time changes of traffic signals and avoid falling
into a local optimum. The collaborative traffic signal control module leverages
a graph attention reinforcement learning framework to extract the latent
features of different intersections and abstract their interplay to learn
cooperative policies. Extensive experiments based on multiple real-world
datasets show that our approach outperforms the state-of-the-art baselines.
