Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

Abstract--In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers. In rapidly expanding metropolitan cities, taxis (which include cars working with ride-sharing platforms such as Uber, Lyft and DiDi) play a vital role in residents' daily commute among all the available modes of transportation [1]. Based on a survey in NYC [2], there is a stable demand of taxis, by 666, 000 passengers per day, which is fulfilled by more than 13, 000 taxis in the region. For these expanding cities, to meet the increasing demand of taxis, an emerging problem is to efficiently utilize the existing road networks to reduce potential traffic congestions and to optimize the effective travel time and distance. One promising solution to this problem is taxi carpool service [3].