Summary of Fairness-enhancing Vehicle Rebalancing in the Ride-hailing System, by Xiaotong Guo et al.
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
by Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, Jinhua Zhao
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a novel vehicle rebalancing method to enhance both algorithmic and rider fairness in the ride-hailing industry. The approach combines Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for demand prediction and Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent rebalancing. This methodology aims to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of the system is evaluated using simulations based on real-world ride-hailing data, showing a 6.48% reduction in standard deviation and 0.49% decrease in average customer wait times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem that affects many people who need ride-hailing services but don’t have access to them because they live in areas where these services are not available or too expensive. The authors want to make sure that the vehicles are moved around to help those who need it most. They developed a new way to do this by using two models together: one for predicting demand and another for moving the vehicles. This method is tested on real data and shows that it can improve fairness and efficiency. |
Keywords
* Artificial intelligence * Convolutional network
