Summary of I-rebalance: Personalized Vehicle Repositioning For Supply Demand Balance, by Haoyang Chen et al.
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
by Haoyang Chen, Peiyan Sun, Qiyuan Song, Wanyuan Wang, Weiwei Wu, Wencan Zhang, Guanyu Gao, Yan Lyu
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed personalized vehicle reposition technique, i-Rebalance, uses deep reinforcement learning (DRL) to balance demand and supply in ride-hailing platforms while considering drivers’ unique cruising preferences and decision-making processes. The approach involves estimating drivers’ acceptance of reposition recommendations through a user study with 99 real drivers, and optimizing supply-demand balance and preference satisfaction simultaneously using a sequential reposition strategy with dual DRL agents. Experimental results show that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ride-hailing platforms want to make sure there are enough cars for people who need rides, but they also want drivers to be happy with their jobs. This paper proposes a new way to do that called i-Rebalance. It uses special computer learning to figure out what drivers like and don’t like about taking rides. The goal is to make sure drivers get good tips and are willing to take on more rides. The researchers tested this idea with real drivers and found that it worked well, increasing driver acceptance by 38% and total income by 10%. |
Keywords
* Artificial intelligence * Reinforcement learning
