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Analysis of taxi demand and supply in New York City: implications of recent taxi regulations

Author

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  • Camille Kamga
  • M. Anil Yazici
  • Abhishek Singhal

Abstract

This paper investigates temporal and weather-related variation in taxi trips in New York City. A taxi trip data-set with 147 million records covering 10 months of activity is used. It is shown that there are substantial variations in ridership, taxi supply, trip distance, and pickup frequency for different time periods and weather conditions. These variations, in turn, cause variations in driver revenues which is one of the main measures of taxi supply-demand equilibrium. The findings are then used to discuss the anticipated impacts of two recently enacted taxi regulation changes: the first fare increase since 2006 and the E-Hail pilot program which allows taxi hailing with smart phone applications. The fare increase is estimated to cause varying levels of revenue increase for different time periods. E-Hail apps are not expected to offer considerable improvements at all times, but rather when both adequate taxi supply and demand occur simultaneously.

Suggested Citation

  • Camille Kamga & M. Anil Yazici & Abhishek Singhal, 2015. "Analysis of taxi demand and supply in New York City: implications of recent taxi regulations," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(6), pages 601-625, August.
    • Handle: RePEc:taf:transp:v:38:y:2015:i:6:p:601-625
    DOI: 10.1080/03081060.2015.1048944
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    Citations

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    Cited by:

    1. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    2. Cetin, Tamer & Deakin, Elizabeth, 2019. "Regulation of taxis and the rise of ridesharing," Transport Policy, Elsevier, vol. 76(C), pages 149-158.
    3. Long Chen & Chenglu Yang & Peng Jing & Qifen Zha & Xingyue Wang & Weichao Wang, 2023. "Are they willing to switch from non-driving to driving? An exploratory study among Chinese older people," Transportation, Springer, vol. 50(4), pages 1125-1163, August.
    4. 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).
    5. Xiong, Ziyue & Jian Li, & Wu, Hangbin, 2021. "Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen," Transport Policy, Elsevier, vol. 101(C), pages 100-118.
    6. Man Zhang & Dongwei Tian & Jingming Liu & Xuehua Li, 2024. "Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    7. Liao, Yuan, 2021. "Ride-sourcing compared to its public-transit alternative using big trip data," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. Wenbo Zhang & Tho V. Le & Satish V. Ukkusuri & Ruimin Li, 2020. "Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems," Transportation, Springer, vol. 47(2), pages 971-996, April.
    9. Willis, George & Tranos, Emmanouil, 2020. "Using ‘Big Data’ to understand the impacts of Uber on taxis in New York City," SocArXiv 25fxs, Center for Open Science.
    10. Suthikasem Weladee & Peamsook Sanit, 2023. "The Spatial Distribution of Taxi Stations in Bangkok," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    11. Lei, Yiyuan & Ozbay, Kaan, 2021. "A robust analysis of the impacts of the stay-at-home policy on taxi and Citi Bike usage: A case study of Manhattan," Transport Policy, Elsevier, vol. 110(C), pages 487-498.

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