Urban Taxi Dispatching Optimization: A Reinforcement Learning-Based Approach

With the expansion of urban scale and the increase motor vehicles, traffic congestion is becoming more serious. The development of public transportation is the main way to alleviate congestion. As part of public transportation, taxis are popular because of their all-weather, comfort and high accessibility, but currently they mainly rely on driver experience to find passengers, resulting in imbalance between supply and demand, affecting passenger experience. In this paper, we propose a taxi dispatch model using reinforcement learning method, which utilizes the Dueling DQN algorithm to optimize the dispatch strategy to alleviate the imbalance between supply and demand. Using the GPS data of taxis in Shenzhen, after data cleaning and analysis, the ARIMA model is used to predict the future boarding and alighting volume, and a supply and demand matrix is constructed. Experiments show that the dispatch efficiency of Dueling DQN can reach 100%, and the average dispatch efficiency is 85.3%.