Data-Driven Robust Taxi Dispatch under Demand Uncertainties

Fei Miao, Shuo Han, Shan Lin, Qian Wang, John A. Stankovic, Abdeltawab Hendawi, Desheng Zhang, Tian He, George J. Pappas

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in realtime. They provide knowledge of system models on passenger demand and mobility patterns for efficient taxi dispatch and coordination strategies. Such approaches face new challenges: how to deal with uncertainties of predicted customer demand while fulfilling the system's performance requirements, including minimizing taxis' total idle mileage and maintaining service fairness across the whole city; how to formulate a computationally tractable problem. To address this problem, we develop a data-driven robust taxi dispatch framework to consider spatial-Temporally correlated demand uncertainties. The robust vehicle dispatch problem we formulate is concave in the uncertain demand and convex in the decision variables. Uncertainty sets of random demand vectors are constructed from data based on theories in hypothesis testing, and provide a desired probabilistic guarantee level for the performance of robust taxi dispatch solutions. We prove equivalent computationally tractable forms of the robust dispatch problem using the minimax theorem and strong duality. Evaluations on four years of taxi trip data for New York City show that by selecting a probabilistic guarantee level at 75%, the average demand-supply ratio error is reduced by 31.7%, and the average total idle driving distance is reduced by 10.13% or about 20 million miles annually, compared with nonrobust dispatch solutions.

Original languageEnglish (US)
Article number8105899
Pages (from-to)175-191
Number of pages17
JournalIEEE Transactions on Control Systems Technology
Issue number1
StatePublished - Jan 2019

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  • computationally tractable approximation
  • data-driven robust optimization
  • demand uncertainties
  • probabilistic guarantee
  • resource allocation
  • taxi dispatch framework


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