Flexible Online Task Assignment in Real-Time Spatial Data
Summary: Defines Flexible Two-sided Online task Assignment (FTOA) for real-time spatial data, guiding idle workers via predicted task/worker distributions to boost matches. Two-step framework combines offline prediction with POLAR-OP online assignment; achieves 0.47-competitive ratio and O(1) per-event processing, validated on synthetic and real taxi data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yongxin Tong
- 2. Libin Wang
- 3. Zimu Zhou
- 4. Bolin Ding
- 5. Lei Chen
- 6. Jieping Ye
- 7. Ke Xu
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,332 | Order Dispatch in Price-aware Ridesharing | 2018 | VLDB | 9.0171784e-05 |
| 6,006 | Last-Mile Delivery Made Practical: An Efficient Route Planning Framework with Theoretical Guarantees | 2020 | VLDB | 5.2415551e-05 |
| 6,631 | Demand-Aware Route Planning for Shared Mobility Services | 2020 | VLDB | 4.9856578e-05 |
| 9,443 | Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach | 2018 | SIGMOD | 4.3408772e-05 |
| 11,302 | Privacy-preserving Cooperative Online Matching over Spatial Crowdsourcing Platforms | 2023 | VLDB | 4.1945683e-05 |
| 11,413 | Online Ridesharing with Meeting Points | 2022 | VLDB | 4.1945683e-05 |
| 13,249 | A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing | 2021 | VLDB | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,085 | Capacity Constrained Assignment in Spatial Databases | 2008 | SIGMOD | 9.5804907e-05 |
| 2,179 | A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing | 2014 | VLDB | 9.3558219e-05 |
| 2,247 | On Efficient Spatial Matching | 2007 | VLDB | 9.2048219e-05 |
| 3,235 | Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis | 2016 | VLDB | 7.3355287e-05 |
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