Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions

Abstract

Personalized route recommendation aims to recommend routes based on users’ route preference. The vast amount of GPS trajectories tracking driving behavior has made deep learning, especially inverse reinforcement learning (IRL), a popular choice for personalized route recommendation. However, current IRL studies assume that the traffic condition is static and approximate the expected state visitation frequencies to update the neural network. This study improves the IRL to recommend personalized routes considering real-time traffic conditions. We also improve the expected state visitation frequency calculation based on characteristics of ride-hailing and taxi trajectories to calculate the gradient of the neural network. In addition, the graph attention network is employed to capture the spatial dependencies between road segments. Numerical experiments using real ride-hailing trajectories in Chengdu, China validate our model. At last, a statistical test is conducted, and route preferences reflected by the same driver’s empty trajectories and occupied trajectories are found to have significant differences.