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Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China

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  • Zheng, Hongyu
  • Zhang, Kenan
  • Nie, Yu (Marco)

Abstract

This paper traces the plunge and rebound of the taxi market in Shenzhen, China through the COVID-19 lockdown. A four-week taxi GPS trajectory data set is collected in the first quarter of 2020, which covers the period of lockdown and phased reopening in the city. We conduct a spatiotemporal analysis of taxi demand using the data, and then select taxis that continued to operate through the analysis period to examine whether and how they adjusted operational strategies. We find, among other things: (i) the taxi demand in Shenzhen shrank more than 85% in the lockdown phase and barely recovered from that bottom even after the city began to reopen; (ii) the recovery of taxi travel fell far behind that of the overall vehicle travel in the city; (iii) most taxis significantly cut back work hours in response to the lockdown, and many adjusted work schedule to focus on serving peak-time demand after it was lifted; (iv) taxi drivers demonstrate distinct behavioral adaptations to the pandemic that can be identified by a clustering analysis; and (v) while the level of taxi service dropped precipitately at the beginning, it quickly rebounded to exceed the pre-pandemic level, thanks to the government’s incentive policy. These empirical findings suggest (i) incentives aiming at boosting supply should more precisely target where the boost is most needed; (ii) the taxi market conditions should be closely monitored to support and adjust policies; and (iii) when the demand is severely depressed by lockdown orders or when the market is oversupplied, taxi drivers should be encouraged and aided to use more centralized dispatching modes.

Suggested Citation

  • Zheng, Hongyu & Zhang, Kenan & Nie, Yu (Marco), 2021. "Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 349-366.
  • Handle: RePEc:eee:transa:v:150:y:2021:i:c:p:349-366
    DOI: 10.1016/j.tra.2021.06.012
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    References listed on IDEAS

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    1. Gérard P. Cachon & Kaitlin M. Daniels & Ruben Lobel, 2017. "The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 368-384, July.
    2. Oum, Tae Hoon & Wang, Kun, 2020. "Socially optimal lockdown and travel restrictions for fighting communicable virus including COVID-19," Transport Policy, Elsevier, vol. 96(C), pages 94-100.
    3. Xie, Jun & (Marco) Nie, Yu & Liu, Xiaobo, 2017. "Testing the proportionality condition with taxi trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 583-601.
    4. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    5. Qian, Xinwu & Ukkusuri, Satish V., 2021. "Connecting urban transportation systems with the spread of infectious diseases: A Trans-SEIR modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 185-211.
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    2. Sen Li & Shitai Bao & Ceyi Yao & Lan Zhang, 2022. "Exploring the Spatio-Temporal and Behavioural Variations in Taxi Travel Based on Big Data during the COVID-19 Pandemic: A Case Study of New York City," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
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    4. Soria, Jason & Edward, Deirdre & Stathopoulos, Amanda, 2023. "Requiem for transit ridership? An examination of who abandoned, who will return, and who will ride more with mobility as a service," Transport Policy, Elsevier, vol. 134(C), pages 139-154.

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