Ambulance Dispatching Algorithm in Shenzhen Based on Deep Reinforcement Learning

Abstract

In prehospital emergency, the emergency response time refers to the time when the ambulance arrives at the scene after the patient dials the emergency phone number.The traditional ambulance dispatching algorithm does not fully consider the dynamics and complexity factors of the emergency environment, resulting in the deviation between the optimization emergenay response time of the model and the actual situation.The ambulance dispatching problem is modeled as a Markov Decision Process (MDP), and an ambulance dispatching algorithm based on deep reinforcement learning is constructed.The multilayer perceptron is used as the scoring network structure, and the dynamic information of the emergency station is mapped to the scores of each emergency station to determine the probability of the ambulance being transferred to each emergency station.Combined with the dynamic decision-making characteristics of ambulance dispatching, the Proximal Policy Optimization (PPO) algorithm under the Actor-Critic framework in reinforcement learning is used to improve the parameters of the scoring network.The experimental results for an actual emergency dataset of the Shenzhen center for prehospital care show that compared with the Fixed, DSM, MEXCLP, and other algorithms, the emergency response time of this algorithm in each emergency event is shortened by approximately 80 s on average, and the average arrival proportion of emergency vehicles within 10 min is 36.5%.The ambulances are dispatched to the appropriate emergency stations in real time.