Data-Driven Robust Taxi Dispatch Under Demand Uncertainties
| Autor: | John A. Stankovic, Shan Lin, Qian Wang, George J. Pappas, Shuo Han, Fei Miao, Abdeltawab M. Hendawi, Tian He, Desheng Zhang |
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| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Mathematical optimization Computer science business.industry Minimax theorem 05 social sciences Probabilistic logic Systems and Control (eess.SY) 02 engineering and technology Data-driven 020901 industrial engineering & automation Control and Systems Engineering Robustness (computer science) Public transport 0502 economics and business 11. Sustainability FOS: Electrical engineering electronic engineering information engineering Computer Science - Systems and Control Strong duality Algorithm design Electrical and Electronic Engineering business Statistical hypothesis testing |
| Zdroj: | IEEE Transactions on Control Systems Technology. 27:175-191 |
| ISSN: | 2374-0159 1063-6536 |
| DOI: | 10.1109/tcst.2017.2766042 |
| Popis: | In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in real-time. 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 non-robust dispatch solutions. Accepted as a regular paper, IEEE Transactions on Control Systems Technology; 15 pages. This version updated as of Oct 2017 |
| Databáze: | OpenAIRE |
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