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Regulation strategies of ride-hailing market in China: an evolutionary game theoretic perspective

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Abstract

With the popularity of the sharing economy, ride-hailing services have greatly affected people’s travel and become a new travel mode for urban residents. However, the lack of effective industry regulation has resulted in serious operational problems and growing difficulties in the furthering development of ride-hailing services in China. Therefore, it is necessary to study the regulation strategies of multiple subjects involved in ride-hailing industry. Based on evolutionary game theory, the paper establishes the tripartite evolution game model about regulation strategies of ride-hailing industry. The theoretical research and simulation results show that the evolutionarily stable strategy of a single subject (Transportation Network Company, driver or passenger) is affected by the strategies of other two subjects together. Moreover, when making the decision, the Transportation Network Companies (TNCs) need to consider the difference between benefits and costs, user scale, incentives and penalties from the government. Drivers need to consider their benefits and costs, travel user scale and penalties from the government and the TNCs. Besides, the benefits and costs, and the harmony of ride-hailing industry need to be considered for passengers. Potential policy implications are proposed.

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Notes

  1. China, The Credit Assessment Method of Taxi Service Quality, §6(31,32) (2018).

  2. China, Interim Measures for the Administration of Online Taxi Booking Business Operations and Services, §6(34, 35, 37) (2016). China, The Credit Assessment Method of Taxi Service Quality, §6(33) (2018).

  3. China, The Credit Assessment Method of Taxi Service Quality, §6(34) (2018).

  4. China, The Credit Assessment Method of Taxi Service Quality, §6(35,36) (2018).

  5. China, Interim Measures for the Administration of Online Taxi Booking Business Operations and Services, §6(36, 37) (2016).

References

  1. Ganapati, S., & Reddick, C. G. (2018). Prospects and challenges of sharing economy for the public sector. Government Information Quarterly,35(1), 77–87.

    Google Scholar 

  2. Zuo, W., Zhu, W., Chen, S., & He, X. (2019). Service quality management of online car-hailing based on PCN in the sharing economy. Electronic Commerce Research and Applications,34, 100827.

    Google Scholar 

  3. Nguyen-Phuoc, D. Q., Su, D. N., Tran, P. T. K., Le, D.-T. T., & Johnson, L. W. (2020). Factors influencing customer’s loyalty towards ride-hailing taxi services—A case study of Vietnam. Transportation Research Part A: Policy and Practice,134, 96–112.

    Google Scholar 

  4. Rayle, L., Dai, D., Chan, N., Cervero, R., & Shaheen, S. (2016). Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transport Policy,45, 168–178.

    Google Scholar 

  5. The Chinese Ministry of Transport. (2016). Interim measures for the administration of online taxi booking business operations and services. Retrieved July 28, 2016, from http://zizhan.mot.gov.cn/zfxxgk/bnssj/zcfgs/201607/t20160728_2068633.html.

  6. Jin, S. T., Kong, H., Wu, R., & Sui, D. Z. (2018). Ridesourcing, the sharing economy, and the future of cities. Cities,76, 96–104.

    Google Scholar 

  7. The Chinese Ministry of Transport. (2018). The credit assessment method of taxi service quality. Retrieved May 24, 2018, from http://www.crta.org.cn/article-3447.html.

  8. Yu, J. J., Tang, C. S., Shen, Z. J. M., & Chen, X. (2019). A balancing act of regulating on-demand ride services. Management Science. https://ssrn.com/abstract=2990209.

  9. Zhang, G., Ma, L., Zhang, X., Ding, X. Y., & Yang, Y. P. (2019). Risk perception and intention to discontinue use of ride-hailing services in China: Taking the example of DiDi Chuxing. Transportation Research Part F: Traffic Psychology and Behavior,66, 459–470.

    Google Scholar 

  10. Guo, Y., Xin, F., & Li, X. (2019). The market impacts of sharing economy entrants: Evidence from USA and China. Electronic Commerce Research. https://doi.org/10.1007/s10660-018-09328-1.

    Article  Google Scholar 

  11. Wadud, Z. (2020). An examination of the effects of ride-hailing services on airport parking demand. Journal of Air Transport Management,84, 101783.

    Google Scholar 

  12. David, E., & Jon, K. (2013). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.

    Google Scholar 

  13. Weibull, J. W. (1998). Evolution, rationality and equilibrium in games. European Economic Review,42(3), 641–649.

    Google Scholar 

  14. Watanabe, C., Naveed, K., & Neittaanmäki, P. (2016). Co-evolution of three mega-trends nurtures un-captured GDP—Uber’s ride-sharing revolution. Technology in Society,46, 164–185.

    Google Scholar 

  15. Wang, Y., Wang, S., Wang, J., Wei, J. C., & Wang, C. L. (2018). An empirical study of consumers’ intention to use ride-sharing services: Using an extended technology acceptance model. Transportation. https://doi.org/10.1007/s11116-018-9893-4.

    Article  Google Scholar 

  16. Amirkiaee, S. Y., & Evangelopoulos, N. (2018). Why do people rideshare? An experimental study. Transportation Research Part F: Traffic Psychology & Behaviour,55, 9–24.

    Google Scholar 

  17. Lavieri, P. S., & Bhat, C. R. (2019). Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips. Transportation Research Part C: Emerging Technologies,105, 100–125.

    Google Scholar 

  18. Tirachini, A., & Gomez-Lobo, A. (2019). Ride-hailing in Santiago de Chile: Users’ characterization and effects on travel behavior. Transport Policy,82, 46–57.

    Google Scholar 

  19. Sui, Y., Zhang, H., Song, X., Shao, F., Yu, X., et al. (2019). GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions. Journal of Cleaner Production,227, 495–505.

    Google Scholar 

  20. Cetin, T., & Deakin, E. (2019). Regulation of taxis and the rise of ridesharing. Transport Policy,76, 149–158.

    Google Scholar 

  21. Nie, Y. (2017). How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China. Transportation Research Part C: Emerging Technologies,79, 242–256.

    Google Scholar 

  22. Sun, L., Teunter, R. H., Babai, M. Z., & Hua, G. (2019). Optimal pricing for ride-sourcing platforms. European Journal of Operational Research,278(3), 783–795.

    Google Scholar 

  23. Contreras, S. D., & Paz, A. (2018). The effects of ride-hailing companies on the taxicab industry in Las Vegas, Nevada. Transportation Research Part A: Policy and Practice,115, 63–70.

    Google Scholar 

  24. Botsman, R., & Rogers, R. (2011). What’s mine is yours: The rise of collaborative consumption. New York: Harper Business.

    Google Scholar 

  25. Wirtz, J., & Tang, C. S. (2016). Uber—Competing as market leader in the U.S. versus being a distant second in China. In J. Wirtz & C. Lovelock (Eds.), Services marketing: People, technology, strategy. Singapore: World Scientific.  https://doi.org/10.1142/9781944659028_0019.

    Chapter  Google Scholar 

  26. Beer, R., Brakewood, C., Rahman, S., & Viscardi, J. (2017). Qualitative analysis of ride-hailing regulations in major American cities. Transportation Research Record: Journal of the Transportation Research Board,2650, 84–91.

    Google Scholar 

  27. Lagadic, M., Verloes, A., & Louvet, N. (2019). Can carsharing services be profitable? A critical review of established and developing business models. Transport Policy,77, 68–78.

    Google Scholar 

  28. Barann, B., Beverungen, D., & Müller, O. (2017). An open-data approach for quantifying the potential of taxi ridesharing. Decision Support Systems,99, 86–95.

    Google Scholar 

  29. Shou, Z., Di, X., Ye, J., Zhu, H., Zhang, H., & Hampshire, R. (2020). Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning. Transportation Research Part C: Emerging Technologies,111, 91–113.

    Google Scholar 

  30. Zeto, W. Y. S., Wong, R. C. P., & Yang, W. H. (2019). A sequential binary logistic regression model is proposed for the dynamic decision-making process of vacant taxi drivers. Transport Policy,76, 100–110.

    Google Scholar 

  31. Xu, Z., Yin, Y., & Ye, J. (2020). On the supply curve of ride-hailing systems. Transportation Research Part B: Methodological,132, 29–43.

    Google Scholar 

  32. Yang, H., Shao, C., Wang, H., & Ye, J. (2020). Optimizing matching time interval and matching radius in on-demand ride-sourcing markets. Transportation Research Part B: Methodological,131, 84–105.

    Google Scholar 

  33. Wang, S., & Smart, M. (2020). The disruptive effect of ridesourcing services on for-hire vehicle drivers’ income and employment. Transport Policy,89, 13–23.

    Google Scholar 

  34. Rogers, B. (2015). The social costs of Uber. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2608017.

    Article  Google Scholar 

  35. Herbert, A. (2016). Portlandia, ridesharing, and sex discrimination. Michigan Law Review,115(18), 18–25.

    Google Scholar 

  36. Hou, D. H. (2015). Legitimacy and supervision system of internet private hire vehicles. Journal of University of Science & Technology Beijing,31(6), 96–103.

    Google Scholar 

  37. Flores, O., & Rayle, L. (2017). How cities use regulation for innovation: The case of Uber, Lyft and sidecar in San Francisco. Transportation Research Procedia,25, 3760–3772.

    Google Scholar 

  38. Bengtsson, N. (2015). Efficient informal trade: Theory and experimental evidence from the Cape Town taxi market. Journal of Development Economics,115, 85–98.

    Google Scholar 

  39. Li, S., Tavafoghi, H., Poolla, K., & Varaiya, P. (2019). Regulating TNCs: Should Uber and Lyft set their own rules? Transportation Research Part B: Methodological,129, 193–225.

    Google Scholar 

  40. Fisher, R. A. (1930). The genetical theory of natural selection: A complete variorum edition. Oxford: Oxford University Press.

    Google Scholar 

  41. Lewontin, R. C. (1976). Evolution and the theory of games. Journal of Theoretical Biology,1(3), 382–403.

    Google Scholar 

  42. Smith, J. M., & Price, G. R. (1973). The logic of animal conflict. Nature,246, 15–18.

    Google Scholar 

  43. Taylor, P. D., & Jonker, L. B. (1978). Evolutionarily stable strategies and game dynamics. Mathematical Biosciences,40(1), 145–156.

    Google Scholar 

  44. Smith, J. M. (1982). Evolution and the theory of games. Cambridge: Cambridge University Press.

    Google Scholar 

  45. Nash, J. F. (1950). Equilibrium points in n-person games. Proceedings of the National Academy of Sciences of the United States of America,36(1), 48–49.

    Google Scholar 

  46. Roca, C. P., Cuesta, J. A., & Sánchez, A. (2009). Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics. Physics of Life Reviews,6(4), 208–249.

    Google Scholar 

  47. Johari, M., Hosseini-Motlagh, S.-M., & Rasti-Barzoki, M. (2019). An evolutionary game theoretic model for analyzing pricing strategy and socially concerned behavior of manufacturers. Transportation Research Part E: Logistics and Transportation Review,128, 506–525.

    Google Scholar 

  48. Damme, E. V. (1987). Evolutionary game theory. Stability and perfection of Nash equilibrium. Berlin: Springer.

    Google Scholar 

  49. Coninx, K., Deconinck, G., & Holvoet, T. (2018). Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics. Applied Energy,218, 104–113.

    Google Scholar 

  50. Chen, S., He, Q., & Xiao, H. (2020). A study on cross-border e-commerce partner selection in B2B mode. Electronic Commerce Research. https://doi.org/10.1007/s10660-020-09403-6100827.

    Article  Google Scholar 

  51. Zhao, T., & Liu, Z. (2019). A novel analysis of carbon capture and storage (CCS) technology adoption: An evolutionary game model between stakeholders. Energy,189, 116352.

    Google Scholar 

  52. Zuo, Y., Zhao, X. G., Zhang, Y. Z., & Zhou, Y. (2019). From feed-in tariff to renewable portfolio standards: An evolutionary game theory perspective. Journal of Cleaner Production,213, 1274–1289.

    Google Scholar 

  53. Liu, Q., Dou, F., & Meng, X. (2019). Effectiveness research on the multi-player evolutionary game of coal-mine safety regulation in China based on system dynamics. Safety Science,111, 224–233.

    Google Scholar 

  54. Mahmoudi, R., & Rasti-Barzoki, M. (2018). Sustainable supply chains under government intervention with a real-world case study: An evolutionary game theoretic approach. Computers & Industrial Engineering,116, 130–143.

    Google Scholar 

  55. Encarnação, S., Santos, F. P., Santos, F. C., Blass, V., Pacheco, J. M., et al. (2018). Paths to the adoption of electric vehicles: An evolutionary game theoretical approach. Transportation Research Part B Methodological,113, 24–33.

    Google Scholar 

  56. Li, J., Jiao, J., & Tang, Y. (2019). An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network. Energy Policy,129, 1–12.

    Google Scholar 

  57. Iwamura, Y., & Tanimoto, J. (2018). Complex traffic flow that allows lane-changing and hampering intrinsically contains social-dilemma structures. Journal of Statistical Mechanics: Theory and Experiment. https://doi.org/10.1088/1742-5468/aaa8ff.

    Article  Google Scholar 

  58. Lei, L. C., & Gao, S. (2019). Transportation network companies and drivers dilemma in China: An evolutionary game theoretic perspective. Transport,34(05), 579–590.

    Google Scholar 

  59. Sun, Q. P., He, Y. Q., Wang, Y. J., & Ma, F. (2019). Evolutionary game between government and ride-hailing platform: Evidence from China. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2019/9545102.

    Article  Google Scholar 

  60. Nisan, N., Roughgarden, T., Tardos, E., & Vazirani, V. V. (2007). Algorithmic game theory. Cambridge: Cambridge University Press.

    Google Scholar 

  61. Selten, R. (1980). A note on evolutionarily stable strategies in asymmetric animal conflicts. Journal of Theoretical Biology,84(1), 93–101.

    Google Scholar 

  62. Ritzberger, K., & Weibull, W. (1996). Evolutionary selection in normal form games. Econometrica,63(6), 71–99.

    Google Scholar 

  63. Friedman, A. (1991). A simple testable model of double auction markets. Journal of Economic Behavior & Organization,15(1), 47–70.

    Google Scholar 

  64. Lyapunov, A. M. (1994). The general problem of the stability of motion. International Journal of Control,31(3), 353–354.

    Google Scholar 

  65. Xiong, Huiyuan, Zhu, Xionglai, & Zhang, Ronghui. (2018). Energy recovery strategy numerical simulation for dual axle drive pure electric vehicle based on motor loss model and big data calculation. Complexity,2018, 1–14.

    Google Scholar 

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Acknowledgements

The authors are very grateful to the anonymous referees for their insightful, constructive, and valuable comments and suggestions. The research was partially supported by the Ministry of Education Layout Foundation of Humanities and Social Sciences under Grant number 19YJA630030, the Social Science Foundation of Hunan Province under Grant Number 17YBA369, and the National Natural Science Foundation of China under Grant Number 71801185.

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  1. Business School, Xiangtan University, Xiangtan, 411105, China

    Li-cai Lei & En-yu Zeng

  2. School of Management and Engineering, Nanjing University, Nanjing, 210093, China

    Shang Gao

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Correspondence to Shang Gao.

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Lei, Lc., Gao, S. & Zeng, Ey. Regulation strategies of ride-hailing market in China: an evolutionary game theoretic perspective. Electron Commer Res 20, 535–563 (2020). https://doi.org/10.1007/s10660-020-09412-5

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