IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p15418-d978376.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/15418/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/15418/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Wills, Hugh, 1987. "A note on specification tests for the multinomial logit model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 263-274.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.
    2. Xiaoyu Xia & Juin Kuan Chong, 2022. "No Magic for Market Entry in the Field: Evidence from Taxi Markets," Management Science, INFORMS, vol. 68(6), pages 4670-4684, June.
    3. Wang, Wei & Miao, Wei & Liu, Yongdong & Deng, Yiting & Cao, Yunfei, 2022. "The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 128-141.
    4. Berger, Thor & Chen, Chinchih & Frey, Carl Benedikt, 2018. "Drivers of disruption? Estimating the Uber effect," European Economic Review, Elsevier, vol. 110(C), pages 197-210.
    5. Sun, Hao & Wang, Hai & Wan, Zhixi, 2019. "Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 76-93.
    6. Nicholas Buchholz & Laura Doval & Jakub Kastl & Filip Matejka & Tobias Salz, 2025. "Personalized Pricing and the Value of Time: Evidence From Auctioned Cab Rides," Econometrica, Econometric Society, vol. 93(3), pages 929-958, May.
    7. 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.
    8. Yiyuan Ma & Ke Chen & Youzhi Xiao & Rong Fan, 2022. "Does Online Ride-Hailing Service Improve the Efficiency of Taxi Market? Evidence from Shanghai," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    9. Buchholz, Nicholas & Doval, Laura & Kastl, Jakub & Matejka, Filip & Salz, Tobias, 2020. "The Value of Time: Evidence From Auctioned Cab Rides," CEPR Discussion Papers 14666, C.E.P.R. Discussion Papers.
    10. Sanae Tashiro & Stephen Choi, 2021. "Labor market outcomes under digital platform business models in the sharing economy: the case of the taxi services industry," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 240-251, October.
    11. Brodeur, Abel & Nield, Kerry, 2018. "An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 1-16.
    12. 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..
    13. Ala Avoyan & Robizon Khubulashvili & Giorgi Mekerishvili, 2020. "Call It a Day: History Dependent Stopping Behavior," CESifo Working Paper Series 8603, CESifo.
    14. Yingjie Zhang & Beibei Li & Sean Qian, 2023. "Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market," Information Systems Research, INFORMS, vol. 34(4), pages 1775-1790, December.
    15. Bajo-Buenestado, Raúl & Borrella-Mas, Miguel Ángel, 2025. "Market competition and the adoption of clean technology: Evidence from the taxi industry," European Economic Review, Elsevier, vol. 174(C).
    16. Doran, Kirk, 2014. "Are long-term wage elasticities of labor supply more negative than short-term ones?," Economics Letters, Elsevier, vol. 122(2), pages 208-210.
    17. Jean-Michel Benkert, 2015. "Bilateral trade with loss-averse agents," ECON - Working Papers 188, Department of Economics - University of Zurich, revised Jul 2022.
    18. Karle, Heiko & Schumacher, Heiner & Vølund, Rune, 2023. "Consumer loss aversion and scale-dependent psychological switching costs," Games and Economic Behavior, Elsevier, vol. 138(C), pages 214-237.
    19. A. Banerji & Neha Gupta, 2011. "Do Auction Bids Betray Expectations-Based Reference Dependent Preferences? A Test, Experimental Evidence, And Estimates Of Loss Aversion," Working papers 206, Centre for Development Economics, Delhi School of Economics.
    20. Andreas Leibbrandt, 2016. "Behavioral Constraints on Pricing: Experimental Evidence on Price Discrimination and Customer Antagonism," CESifo Working Paper Series 6214, CESifo.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15418-:d:978376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.