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Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

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  • Rongxiang Su

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Luoyu Road 129, Wuhan 430079, China)

  • Zhixiang Fang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Luoyu Road 129, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China)

  • Ningxin Luo

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Luoyu Road 129, Wuhan 430079, China)

  • Jingwei Zhu

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Luoyu Road 129, Wuhan 430079, China)

Abstract

The locations where taxicabs pick up and drop off passengers are crucial to understanding the dynamics of taxi trip demand. Investigating their spatial distribution and derived dynamic features is still a key task in the fields of urban geography and transportation. Such investigations are urgently needed, considering the competition created by new communication technology services, specifically e-hailing apps such as Uber, Didi and Kuaidi. For example, a subsidy war between two e-hailing apps occurred in China in 2014. However, how the pick-up and drop-off locations of taxicabs change during subsidy wars is still an open question. This paper introduces a methodological framework that can be used to derive the pick-up and drop-off dynamics of taxicabs. It also proposes three indexes that can be used to assess the dynamics of the pick-up and drop-off locations at the city and sub-district scales, namely the numbers of daily pick ups and drop offs per taxi, average transfer distance per unit area of weighted mean centers of pick-up and drop-off locations, and degree of dispersion in the spatial distribution of pick-up and drop-off locations. This paper employs data from taxicabs in the city of Shenzhen to uncover the dynamics of their pick-up and drop-off locations. The results show that the methodological framework and the indexes introduced are powerful tools for uncovering the dynamics of the pick-up and drop-off locations of taxicabs in urban environments.

Suggested Citation

  • Rongxiang Su & Zhixiang Fang & Ningxin Luo & Jingwei Zhu, 2018. "Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps," Sustainability, MDPI, vol. 10(4), pages 1-24, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1256-:d:142083
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    as
    1. Kim, Young-Joo & Hwang, Hark, 2008. "Incremental discount policy for taxi fare with price-sensitive demand," International Journal of Production Economics, Elsevier, vol. 112(2), pages 895-902, April.
    2. Kang, Chaogui & Ma, Xiujun & Tong, Daoqin & Liu, Yu, 2012. "Intra-urban human mobility patterns: An urban morphology perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1702-1717.
    3. Li, Qingquan & Zhang, Tong & Wang, Handong & Zeng, Zhe, 2011. "Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach," Journal of Transport Geography, Elsevier, vol. 19(3), pages 379-393.
    4. Sean D. Barrett, 2003. "Regulatory Capture, Property Rights And Taxi Deregulation: A Case Study," Economic Affairs, Wiley Blackwell, vol. 23(4), pages 34-40, December.
    5. Wong, R.C.P. & Szeto, W.Y. & Wong, S.C., 2014. "Bi-level decisions of vacant taxi drivers traveling towards taxi stands in customer-search: Modeling methodology and policy implications," Transport Policy, Elsevier, vol. 33(C), pages 73-81.
    6. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    7. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    8. Zhang, Shen & Tang, Jinjun & Wang, Haixiao & Wang, Yinhai & An, Shi, 2017. "Revealing intra-urban travel patterns and service ranges from taxi trajectories," Journal of Transport Geography, Elsevier, vol. 61(C), pages 72-86.
    9. Cascajo, Rocío & Diaz Olvera, Lourdes & Monzon, Andrés & Plat, Didier & Ray, Jean-Baptiste, 2018. "Impacts of the economic crisis on household transport expenditure and public transport policy: Evidence from the Spanish case," Transport Policy, Elsevier, vol. 65(C), pages 40-50.
    10. Khalilikhah, Majid & Habibian, Meeghat & Heaslip, Kevin, 2016. "Acceptability of increasing petrol price as a TDM pricing policy: A case study in Tehran," Transport Policy, Elsevier, vol. 45(C), pages 136-144.
    11. Ann Schwarz-Miller & Wayne K. Talley, 2003. "Effects of Public Transit Policies on Taxi Drivers' Wages," Journal of Labor Research, Transaction Publishers, vol. 24(1), pages 131-142, January.
    12. Fang, Zhixiang & Shaw, Shih-Lung & Tu, Wei & Li, Qingquan & Li, Yuguang, 2012. "Spatiotemporal analysis of critical transportation links based on time geographic concepts: a case study of critical bridges in Wuhan, China," Journal of Transport Geography, Elsevier, vol. 23(C), pages 44-59.
    13. Rocío Cascajo & Lourdes Diaz Olvera & Andrés Monzon & Didier Plat & Jean-Baptiste Ray, 2018. "Impacts of the economic crisis on household transport expenditure and public transport policy: Evidence from the Spanish case," Post-Print halshs-01672812, HAL.
    14. Yao, Can-Zhong & Lin, Ji-Nan, 2016. "A study of human mobility behavior dynamics: A perspective of a single vehicle with taxi," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 51-58.
    15. Jonathan V. Hall & Alan B. Krueger, 2015. "An Analysis of the Labor Market for Uber's Driver-Partners in the United States," Working Papers 587, Princeton University, Department of Economics, Industrial Relations Section..
    16. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    17. Çetin, Tamer & Yasin Eryigit, Kadir, 2013. "The economic effects of government regulation: Evidence from the New York taxicab market," Transport Policy, Elsevier, vol. 25(C), pages 169-177.
    18. Sharaby, Nir & Shiftan, Yoram, 2012. "The impact of fare integration on travel behavior and transit ridership," Transport Policy, Elsevier, vol. 21(C), pages 63-70.
    19. Liang, Xiao & Zheng, Xudong & Lv, Weifeng & Zhu, Tongyu & Xu, Ke, 2012. "The scaling of human mobility by taxis is exponential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2135-2144.
    20. Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
    21. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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    Cited by:

    1. Shuxin Jin & Juan Su & Zhouhao Wu & Di Wang & Ming Cai, 2022. "What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    2. Wong, R.C.P. & Szeto, W.Y., 2022. "The effects of peak hour and congested area taxi surcharges on customers’ travel decisions: Empirical evidence and policy implications," Transport Policy, Elsevier, vol. 121(C), pages 78-89.
    3. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    4. Ting Wang & Yong Zhang & Meiye Li & Lei Liu, 2019. "How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(23), pages 1-18, November.

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