A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing
Summary: Queueing-theoretic framework for MRVD in dynamic car-hailing; proves NP-hard and intractable. Leverages ML demand prediction, a double-sided queue for driver idle times, and batch-dispatch to maximize expected revenue, achieving 3–10% gains with scalable performance. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Peng Cheng
- 2. Jiabao Jin
- 3. Xuemin Lin
- 4. Lei Chen
- 5. Libin Zheng
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,332 | Order Dispatch in Price-aware Ridesharing | 2018 | VLDB | 9.0171784e-05 |
| 2,520 | A Unified Approach to Route Planning for Shared Mobility | 2018 | VLDB | 8.6069685e-05 |
| 3,235 | Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis | 2016 | VLDB | 7.3355287e-05 |
| 6,743 | Utility-Aware Ridesharing on Road Networks | 2017 | SIGMOD | 4.9430196e-05 |
| 8,543 | Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers | 2015 | VLDB | 4.4937074e-05 |
| 11,794 | Flexible Online Task Assignment in Real-Time Spatial Data | 2017 | VLDB | 4.1945683e-05 |
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