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Dispatching and Pricing in Two-Sided Spatial Queues.

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Τίτλος: Dispatching and Pricing in Two-Sided Spatial Queues.
Συγγραφείς: Xu, Ang1 (AUTHOR) angxu@berkeley.edu, Yan, Chiwei1 (AUTHOR) chiwei@berkeley.edu
Πηγή: Transportation Science (INFORMS). Sep/Oct2026, Vol. 60 Issue 5, p828-847. 20p.
Θεματικοί όροι: *Markov processes, *Queuing theory, *Prices, *Dynamic programming, *Ridesharing services, Matching theory
Περίληψη: We study a dispatching and pricing problem in two-sided spatial queues with fixed supply, motivated by ride-hailing and robotaxi platforms. Idle drivers queue on one side, waiting to pick up riders, while riders queue on the other, waiting to be matched with available drivers. The platform seeks to maximize net profit, penalized by rider waiting penalties, by jointly optimizing state-dependent dispatching and pricing decisions. We formulate this problem as a Markov decision process with state-dependent service times that capture key features of spatial matching. We show that, under mild assumptions, the optimal dispatching policy admits a closed-form expression with a zigzag structure. This policy significantly improves the tractability of pricing optimization because of the resulting closed-form stationary distribution and a substantially reduced state space. Building on this insight, we propose an efficient and scalable dynamic programming heuristic to approximate the optimal zigzag policy in more general settings. Extensive numerical experiments with both the analytical model and ride-hailing simulations demonstrate that our algorithm is both near-optimal and highly scalable. Funding: This work was supported by a University of Washington–Amazon Science Hub Faculty Research Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0319. [ABSTRACT FROM AUTHOR]
Copyright of Transportation Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Dispatching and Pricing in Two-Sided Spatial Queues.
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  Data: <searchLink fieldCode="JN" term="%22Transportation+Science+%28INFORMS%29%22">Transportation Science (INFORMS)</searchLink>. Sep/Oct2026, Vol. 60 Issue 5, p828-847. 20p.
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  Label: Abstract
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  Data: We study a dispatching and pricing problem in two-sided spatial queues with fixed supply, motivated by ride-hailing and robotaxi platforms. Idle drivers queue on one side, waiting to pick up riders, while riders queue on the other, waiting to be matched with available drivers. The platform seeks to maximize net profit, penalized by rider waiting penalties, by jointly optimizing state-dependent dispatching and pricing decisions. We formulate this problem as a Markov decision process with state-dependent service times that capture key features of spatial matching. We show that, under mild assumptions, the optimal dispatching policy admits a closed-form expression with a zigzag structure. This policy significantly improves the tractability of pricing optimization because of the resulting closed-form stationary distribution and a substantially reduced state space. Building on this insight, we propose an efficient and scalable dynamic programming heuristic to approximate the optimal zigzag policy in more general settings. Extensive numerical experiments with both the analytical model and ride-hailing simulations demonstrate that our algorithm is both near-optimal and highly scalable. Funding: This work was supported by a University of Washington–Amazon Science Hub Faculty Research Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0319. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Transportation Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1287/trsc.2025.0319
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 828
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        Type: general
      – SubjectFull: Queuing theory
        Type: general
      – SubjectFull: Prices
        Type: general
      – SubjectFull: Dynamic programming
        Type: general
      – SubjectFull: Ridesharing services
        Type: general
      – SubjectFull: Matching theory
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              M: 09
              Text: Sep/Oct2026
              Type: published
              Y: 2026
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