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What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving

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Listed:
  • Shuxin Jin

    (School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China)

  • Juan Su

    (Institute of Transportation Engineering, Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang’an University, Xi’an 710064, China)

  • Zhouhao Wu

    (School of Civil Engineering, Tsinghua University, Beijing 100084, China)

  • Di Wang

    (Department of Civil and Environmental Engineering, Nagoya University, Nagoya 464-8603, Japan)

  • Ming Cai

    (School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China)

Abstract

The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15418-:d:978376
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    References listed on IDEAS

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    1. Yang, Lin & Kwan, Mei-Po & Pan, Xiaofang & Wan, Bo & Zhou, Shunping, 2017. "Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 1-27.
    2. Vincent P. Crawford & Juanjuan Meng, 2011. "New York City Cab Drivers' Labor Supply Revisited: Reference-Dependent Preferences with Rational-Expectations Targets for Hours and Income," American Economic Review, American Economic Association, vol. 101(5), pages 1912-1932, August.
    3. 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.
    4. Wills, Hugh, 1987. "A note on specification tests for the multinomial logit model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 263-274.
    5. Sihai Zhang & Zhiyang Wang, 2016. "Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-21, November.
    6. Guillaume R. Fréchette & Alessandro Lizzeri & Tobias Salz, 2019. "Frictions in a Competitive, Regulated Market: Evidence from Taxis," American Economic Review, American Economic Association, vol. 109(8), pages 2954-2992, August.
    7. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
    8. 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.
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