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Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case

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  • Anil Yazici, M.
  • Kamga, Camille
  • Singhal, Abhishek

Abstract

Taxi service is an important component of airport ground access, which affects the economic competitiveness of an airport and its potential positive impact on the surrounding region. Airports across the globe experience both taxi shortages and excesses due to various factors such as the airport’s proximity to the city center, timing and frequency of flights, and the fare structure. Since taxi drivers are independent entities whose decisions affect the taxi supply at airports, it is important to understand taxi drivers’ decision mechanisms in order to suggest policies and to maintain taxi demand and supply equilibrium at the airports. In this paper, New York City (NYC) taxi drivers’ decisions about airport pick-ups or cruising for customers at the end of each trip is modeled using logistic regression based on a large taxi GPS dataset. The presented approach helps to quantify the potential impacts of parameters and to rank their influence for policy recommendations. The results reveal that spatial variables (mainly related to proximity) have the highest impact on taxi drivers’ airport pickup decisions, followed by temporal, environmental and driver-shift related variables. Along with supplementary information from unstructured taxi driver interviews, the model results are used to suggest policies for the improvement of John F. Kennedy (JFK) airport’s ground access and passenger satisfaction, i.e. the implementation of taxi driver frequent airport server punch cards and a time-specific ride share program.

Suggested Citation

  • Anil Yazici, M. & Kamga, Camille & Singhal, Abhishek, 2016. "Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 48-60.
    • Handle: RePEc:eee:transa:v:91:y:2016:i:c:p:48-60
    DOI: 10.1016/j.tra.2016.06.004
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    References listed on IDEAS

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    2. Yan, Pengyu & Yu, Kaize & Chao, Xiuli & Chen, Zhibin, 2023. "An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1218-1233.
    3. Rathore, Bhawana & Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay, 2024. "Predicting the price of taxicabs using Artificial Intelligence: A hybrid approach based on clustering and ordinal regression models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    4. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "A bottom-up transportation network efficiency measuring approach: A case study of taxi efficiency in New York City," Journal of Transport Geography, Elsevier, vol. 80(C).
    5. Jia, Wen & Huang, Yu-lin & Zhao, Qun & Qi, Yi, 2022. "Modeling taxi drivers’ decisions at airport based on queueing theory," Research in Transportation Economics, Elsevier, vol. 92(C).
    6. Ugirumurera, Juliette & Severino, Joseph & Ficenec, Karen & Ge, Yanbo & Wang, Qichao & Williams, Lindy & Chae, Junghoon & Lunacek, Monte & Phillips, Caleb, 2021. "A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 130-150.

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