mlsanityData to 7 Oct 2026

A Survey of Machine Learning-Based Ride-Hailing Planning

arXiv 2303.14646 ↗First posted 26 Mar 2023cs.LG cs.AIcited by 3

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Abstract (arXiv)

Ride-hailing is a sustainable transportation paradigm where riders access door-to-door traveling services through a mobile phone application, which has attracted a colossal amount of usage. There are two major planning tasks in a ride-hailing system: (1) matching, i.e., assigning available vehicles to pick up the riders, and (2) repositioning, i.e., proactively relocating vehicles to certain locations to balance the supply and demand of ride-hailing services. Recently, many studies of ride-hailing planning that leverage machine learning techniques have emerged. In this article, we present a comprehensive overview on latest developments of machine learning-based ride-hailing planning. To offer a clear and structured review, we introduce a taxonomy into which we carefully fit the different categories of related works according to the types of their planning tasks and solution schemes, which include collective matching, distributed matching, collective repositioning, distributed repositioning, and joint matching and repositioning. We further shed light on many real-world datasets and simulators that are indispensable for empirical studies on machine learning-based ride-hailing planning strategies. At last, we propose several promising research directions for this rapidly growing research and practical field.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 67 of its 264 references that are in the map. First posted on arXiv 26 Mar 2023.

Builds on

67 of 264 references.

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50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026EXHOLD: Experience-Aware Real-Time Hold Control for Large-Scale Ride-Hailing Matching at DiDicited by 1
  2. 2025Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentivescited by 0
  3. 2026ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDicited by 0