Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
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Abstract (arXiv)
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).
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 6 Nov 2022.
Builds on
10 of 25 references.
References · 10
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- 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
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Citing papers per year: 2023: 2.
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- 2025RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
- 2026D3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market
- 2021Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning
- 2020Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System
- 2022A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
- 2025One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms
- 2026ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDi
- 2019Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning