Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning

TL;DR

This paper introduces an offline deep reinforcement learning approach to optimize demand distribution in ride-hailing services by learning long-term spatio-temporal values, aiming to balance supply and demand effectively.

Contribution

It presents a novel offline deep reinforcement learning method that considers long-term effects of pricing strategies on demand and supply in ride-hailing systems.

Findings

Improved resource utilization in ride-hailing services.

Enhanced customer satisfaction through demand management.

Effective long-term demand-supply balancing achieved.

Abstract

A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a period of time in the future. Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy. In this study, we propose an offline deep reinforcement learning based method focusing on the demand side to improve the utilization of transportation resources and customer satisfaction. We adopt a spatio-temporal learning method to learn the value of different time and location, then incentivize the ride requests of passengers to adjust the distribution of demand to balance the supply and demand in the system. In particular, we model the problem as a Markov Decision Process (MDP).