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research-article

Understanding Taxi Service Strategies From Taxi GPS Traces

Authors: Daqing Zhang, Lin Sun,

Zhaohui WuAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 16, Issue 1

Pages 123 - 135

https://doi.org/10.1109/TITS.2014.2328231

Published: 30 January 2015 Publication History

Abstract

Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,<sup>1</sup> which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.

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Cited By

Ozeki RYonekura HRizk HYamaguchi H(2024)Privacy Preserved Taxi Demand Prediction System for Distributed DataProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691234(123-134)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691234
Pang JAyub Sabir MWang ZHu AYang XYu HHuang Q(2024)Finding a Taxi With Illegal Driver Substitution Activity via Behavior ModelingsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340974425:12(20309-20319)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3409744
Ozeki RYonekura HBaimbetova ARizk HYamaguchi HRenz MNascimento M(2023)One Model Fits All: Cross-Region Taxi-Demand ForecastingProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625651(1-4)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625651
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Index Terms

Understanding Taxi Service Strategies From Taxi GPS Traces
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 16, Issue 1

Feb. 2015

523 pages

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Copyright © 2014.

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IEEE Press

Publication History

Published: 30 January 2015

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Cited By

Ozeki RYonekura HRizk HYamaguchi H(2024)Privacy Preserved Taxi Demand Prediction System for Distributed DataProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691234(123-134)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691234
Pang JAyub Sabir MWang ZHu AYang XYu HHuang Q(2024)Finding a Taxi With Illegal Driver Substitution Activity via Behavior ModelingsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340974425:12(20309-20319)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3409744
Ozeki RYonekura HBaimbetova ARizk HYamaguchi HRenz MNascimento M(2023)One Model Fits All: Cross-Region Taxi-Demand ForecastingProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625651(1-4)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625651
Yan LShen HKang LZhao JZhang ZXu C(2023)MobiCharger: Optimal Scheduling for Cooperative EV-to-EV Dynamic Wireless ChargingIEEE Transactions on Mobile Computing10.1109/TMC.2022.320041422:12(6889-6906)Online publication date: 1-Dec-2023
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Guo SShen QLiu ZChen CChen CWang JLi ZXu K(2023)Seeking Based on Dynamic Prices: Higher Earnings and Better Strategies in Ride-on-Demand ServicesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324304524:5(5527-5542)Online publication date: 1-May-2023
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Zhang RGhanem R(2022)Drivers Learn City-Scale Intra-Daily Dynamic EquilibriumIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.314016323:9(15278-15287)Online publication date: 1-Sep-2022
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Chen CGao LXie XWang Z(2022)Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problemFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-019-8364-114:2(364-377)Online publication date: 11-Mar-2022
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