i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
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arXiv
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| Format: | Preprint |
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2024
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| author | Chen, Haoyang Sun, Peiyan Song, Qiyuan Wang, Wanyuan Wu, Weiwei Zhang, Wencan Gao, Guanyu Lyu, Yan |
| author_facet | Chen, Haoyang Sun, Peiyan Song, Qiyuan Wang, Wanyuan Wu, Weiwei Zhang, Wencan Gao, Guanyu Lyu, Yan |
| contents | Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance Chen, Haoyang Sun, Peiyan Song, Qiyuan Wang, Wanyuan Wu, Weiwei Zhang, Wencan Gao, Guanyu Lyu, Yan Artificial Intelligence Multiagent Systems Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%. |
| title | i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2401.04429 |