Transportation network companies and drivers dilemma in China: an evolutionary game theoretic perspective
Abstract
The ridesourcing services market in China has recently experienced significant changes, which stem from its legalization and management policy. These changes impact multiple stakeholders of this market (e.g., drivers, passengers, government, competing services) and present them with new opportunities and challenges. This paper develops an evolutionary game model to analyse the Evolutionary Stable Strategy (ESS) between the Transportation Network Companies (TNCs) and drivers. The new model is explored and analysed with simulation experiments to observe the dynamic route of multiple stakeholders. The theoretical research and simulation results indicate that under the authorities’ control over the TNCs, when the net income under strict management is higher than that of the loose management for the TNCs, the final ESS is “Legal Operation, Strict Management”. When the net income under strict management is less than that of the loose management for the THCs, the strategy of “Illegal Operation, Loose Management” may gain popularity and continue to grow; in this case, the ESS may also not exist. The model indicates the strength of the government’s control plays a significant role in leading the achievement of “Legal Operation, Strict Management”. As a consequence, to achieve the perfect evolution of “Legal Operation, Strict Management”, it is necessary for the government to impose a greater penalty on illegal drivers and ensure appropriate compensation measures. The results of the study provide a useful reference for the sustainable development of the ridesourcing services market.
First published online 13 September 2019
Keyword : ridesourcing services, transportation network companies, new policy, evolutionary game theory, evolutionary stable strategy
How to Cite
Lei, L., & Gao, S. (2019). Transportation network companies and drivers dilemma in China: an evolutionary game theoretic perspective. Transport, 34(5), 579-590. https://doi.org/10.3846/transport.2019.11105
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Amirkiaee, S. Y.; Evangelopoulos, N. 2018. Why do people rideshare? An experimental study, Transportation Research Part F: Traffic Psychology and Behaviour 55: 9–24. https://doi.org/10.1016/j.trf.2018.02.025
Anderson, D. N. 2014. “Not just a taxi”? For-profit ridesharing, driver strategies, and VMT, Transportation 41(5): 1099–1117. https://doi.org/10.1007/s11116-014-9531-8
Babu, S.; Mohan, U. 2018. An integrated approach to evaluating sustainability in supply chains using evolutionary game theory, Computers & Operations Research 89: 269–283. https://doi.org/10.1016/j.cor.2017.01.008
Barann, B.; Beverungen, D.; Müller, O. 2017. An open-data approach for quantifying the potential of taxi ridesharing, Decision Support Systems 99: 86–95. https://doi.org/10.1016/j.dss.2017.05.008
Bengtsson, N. 2015. Efficient informal trade: theory and experimental evidence from the Cape Town taxi market, Journal of Development Economics 115: 85–98. https://doi.org/10.1016/j.jdeveco.2015.02.003
Botsman, R.; Rogers, R. 2010. What’s Mine is Yours: the Rise of Collaborative Consumption. Harper Business. 304 p.
Caulfield, B. 2009. Estimating the environmental benefits of ridesharing: a case study of Dublin, Transportation Research Part D: Transport and Environment 14(7): 527–531. https://doi.org/10.1016/j.trd.2009.07.008
Cetin, T.; Deakin, E. 2019. Regulation of taxis and the rise of ridesharing, Transport Policy 76: 149–158. https://doi.org/10.1016/j.tranpol.2017.09.002
Chan, N. D.; Shaheen, S. A. 2012. Ridesharing in North America: past, present, and future, Transport Reviews 32(1): 93–112. https://doi.org/10.1080/01441647.2011.621557
Chen, Y.-F. 2017. The regulatory structure of “Internet plus”: a case study on “app-based ride and taxi services”, Jurist (1): 17–31 (in Chinese).
Chen, Z. 2015. Impact of Ride-Sourcing Services on Travel Habits and Transportation Planning. MSc Thesis. University of Pittsburgh, US. 64 p. Available from Internet: http://d-scholarship.pitt.edu/25827
Coninx, K.; Holvoet, T. 2015. Darwin in smart power grids – evolutionary game theory for analyzing self-organization in demand-side aggregation, in 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems, 21– 25 September 2015, Cambridge, MA, US, 101–110. https://doi.org/10.1109/SASO.2015.18
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. https://doi.org/10.1016/j.tra.2017.11.008
Cramer, J.; Krueger, A. B. 2016. Disruptive change in the taxi business: the case of Uber, American Economic Review 106(5): 177–182. https://doi.org/10.1257/aer.p20161002
Easley, D.; Kleinberg, J. 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press. https://doi.org/10.1017/CBO9780511761942
Davis, J. 2015. Drive at your own risk: Uber’s misrepresentations to UberX drivers about insurance coverage violate California’s unfair competition law, Boston College Law Review 56(3): 1097–1142.
Dong, Y.; Wang, S.; Li, L.; Zhang, Z. 2018. An empirical study on travel patterns of internet based ride-sharing, Transportation Research Part C: Emerging Technologies 86: 1–22. https://doi.org/10.1016/j.trc.2017.10.022
Ferguson, E. 1997. The rise and fall of the American carpool: 1970–1990, Transportation 24(4): 349–376. https://doi.org/10.1023/A:1004928012320
Fisher, R. A. 1930. The Genetical Theory of Natural Selection. Oxford University Press. 308 p.
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: 3756–3768. https://doi.org/10.1016/j.trpro.2017.05.232
Friedman, D. 1991. Evolutionary games in economics, Econometrica 59(3): 637–666. https://doi.org/10.2307/2938222
Fritz, C. 2014. Mobility-as-a-Service: Turning Transportation into a Software Industry. Available from Internet: https://venturebeat.com/2014/12/13/mobility-as-a-service-turningtransportation-into-a-software-industry
Glöss, M.; McGregor, M.; Brown, B. 2016. Designing for labour: Uber and the on-demand mobile workforce, in CHI’16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 7–12 May 2016, San Jose, California, US, 1632–1643. https://doi.org/10.1145/2858036.2858476
Harding, S.; Kandlikar, M.; Gulati, S. 2016. Taxi apps, regulation, and the market for taxi journeys, Transportation Research Part A: Policy and Practice 88: 15–25. https://doi.org/10.1016/j.tra.2016.03.009
Henao, A.; Marshall, W. 2017. A Framework for understanding the impacts of ridesourcing on transportation, in G. Meyer, S. Shaheen (Eds.). Disrupting Mobility: Impacts of Sharing Economy and Innovative Transportation on Cities, 197–209. https://doi.org/10.1007/978-3-319-51602-8_13
Herbert, A. 2016. Portlandia, ridesharing, and sex discrimination, Michigan Law Review Online 115: 18–25.
Hou, D.-H. 2015. Legitimacy and supervision system of internet private hire vehicles, Journal of University of Science and Technology Beijing (Social Sciences Edition) (6): 96–103. (in Chinese).
Hughes, R.; MacKenzie, D. 2016. Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators, Journal of Transport Geography 56: 36–44. https://doi.org/10.1016/j.jtrangeo.2016.08.014
Iwamura, Y.; Tanimoto, J. 2018. Complex traffic flow that allows as well as hampers lane-changing intrinsically contains socialdilemma structures, Journal of Statistical Mechanics: Theory and Experiment 2018(2): 023408. https://doi.org/10.1088/1742-5468/aaa8ff
Ji, P.; Ma, X.; Li, G. 2015. Developing green purchasing relationships for the manufacturing industry: an evolutionary game theory perspective, International Journal of Production Economics 166: 155–162. https://doi.org/10.1016/j.ijpe.2014.10.009
Jin, M.; Lei, X.; Du, J. 2010. Evolutionary game theory in multiobjective optimization problem, International Journal of Computational Intelligence Systems 3: 74–87. https://doi.org/10.1080/18756891.2010.9727754
Jin, S. T.; Kong, H.; Wu, R.; Sui, D. Z. 2018. Ridesourcing, the sharing economy, and the future of cities, Cities 76: 96–104. https://doi.org/10.1016/j.cities.2018.01.012
Kelley, K. L. 2007. Casual carpooling-enhanced, Journal of Public Transportation 10(4): 119–130. http://doi.org/10.5038/2375-0901.10.4.6
Kung, L.-C.; Liao, W.-H. 2018. An approximation algorithm for a competitive facility location problem with network effects, European Journal of Operational Research 267(1): 176–186. https://doi.org/10.1016/j.ejor.2017.11.037
Lewontin, R. C. 1961. Evolution and the theory of games, Journal of Theoretical Biology 1(3): 382–403. https://doi.org/10.1016/0022-5193(61)90038-8
Liu, D.; Xiao, X.; Li, H.; Wang, W. 2015a. Historical evolution and benefit–cost explanation of periodical fluctuation in coal mine safety supervision: an evolutionary game analysis framework, European Journal of Operational Research 243(3): 974–984. https://doi.org/10.1016/j.ejor.2014.12.046
Liu, H.; Zhang, F. 2015. The insurance dilemma in new online enabled transportation and legal countermeasures, Insurance Studies (12): 107–113. (in Chinese).
Liu, Q.; Li, X.; Hassall, M. 2015b. Evolutionary game analysis and stability control scenarios of coal mine safety inspection system in China based on system dynamics, Safety Science 80: 13–22. https://doi.org/10.1016/j.ssci.2015.07.005
Lobel, O. 2016. The law of the platform, in Social Science Research Network (SSRN) Electronic paper Collection, San Diego Legal Studies Paper No. 16-212. Available from Internet: https://ssrn.com/abstract=2742380
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. https://doi.org/10.1016/j.cie.2017.12.028
Minett, P.; Pearce, J. 2011. Estimating the energy consumption impact of casual carpooling, Energies 4(1): 126–139. https://doi.org/10.3390/en4010126
Morency, C. 2007. The ambivalence of ridesharing, Transportation 34(2): 239–253. https://doi.org/10.1007/s11116-006-9101-9
Nakata, M.; Yamauchi, A.; Tanimoto, J.; Hagishima, A. 2010. Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction, Physica A: Statistical Mechanics and its Applications 389(23): 5353–5361. https://doi.org/10.1016/j.physa.2010.08.005
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. https://doi.org/10.1073/pnas.36.1.48
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. https://doi.org/10.1016/j.trc.2017.03.017
Nisan, N.; Roughgarden, T.; Tardos, E.; Vazirani, V. V. 2007. Algorithmic Game Theory. Cambridge University Press. 778 p.
Ozkan-Canbolat, E.; Beraha, A.; Bas, A. 2016. Application of evolutionary game theory to strategic innovation, Procedia – Social and Behavioral Science 235: 685–693. https://doi.org/10.1016/j.sbspro.2016.11.069
Pal, R.; Scrimitore, M. 2016. Tacit collusion and market concentration under network effects, Economics Letters 145: 266–269. https://doi.org/10.1016/j.econlet.2016.07.005
Ramezani, M.; Nourinejad, M. 2017. Dynamic modeling and control of taxi services in large-scale urban networks: a macroscopic approach, Transportation Research Procedia 23: 41–60. https://doi.org/10.1016/j.trpro.2017.05.004
Rasouli, S.; Timmermans, H. 2014. Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research, Travel Behaviour and Society 1(3): 79–90. https://doi.org/10.1016/j.tbs.2013.12.001
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. https://doi.org/10.1016/j.tranpol.2015.10.004
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. https://doi.org/10.1016/j.plrev.2009.08.001
Rogers, B. 2015. The social costs of Uber, in Social Science Research Network (SSRN) Electronic paper Collection, Temple University Legal Studies Research Paper No. 2015-28. https://doi.org/10.2139/ssrn.2608017
Smith, J. M. 1982. Evolution and the Theory of Games. Cambridge University Press. 234 p.
Smith, J. M.; Price, G. R. 1973. The logic of animal conflict, Nature 246: 15–18. https://doi.org/10.1038/246015a0
Song, X.; Zhang, X. 2017. The cost benefit analysis in local government regulation on the car-hailing: illustrated by the case of Beijing, Journal of Chinese Academy of Governance (05): 123–130. (in Chinese).
Tan, J. 2016. Redefine Sharing: Sharing Practice of Uber (in China). Beijing: China Friendship Press. (in Chinese).
Tanimoto, J. 2015. Fundamentals of Evolutionary Game Theory and its Applications. Springer. 214 p. https://doi.org/10.1007/978-4-431-54962-8
Tanimoto, J.; Fujiki, T.; Wang, Z.; Hagishima, A.; Ikegaya, N. 2014a. Dangerous drivers foster social dilemma structures hidden behind a traffic flow with lane changes, Journal of Statistical Mechanics: Theory and Experiment 2014(11): P11027. https://doi.org/10.1088/1742-5468/2014/11/P11027
Tanimoto, J.; Kukida, S.; Hagishima, A. 2014b. Social dilemma structures hidden behind traffic flow with lane changes, Journal of Statistical Mechanics: Theory and Experiment 2014(7): P07019. https://doi.org/10.1088/1742-5468/2014/07/P07019
Tanimoto, J.; Nakamura, K. 2016. Social dilemma structure hidden behind traffic flow with route selection, Physica A: Statistical Mechanics and its Applications 459: 92–99. https://doi.org/10.1016/j.physa.2016.04.023
Tanimoto, J.; Sagara, H. 2011. Social diffusive impact analysis based on evolutionary computations for a novel car navigation system sharing individual information in urban traffic systems, Journal of Navigation 64(4): 711–725. https://doi.org/10.1017/S037346331100021X
Taylor, P. D.; Jonker, L. B. 1978. Evolutionary stable strategies and game dynamics, Mathematical Biosciences 40(1–2): 145–156. https://doi.org/10.1016/0025-5564(78)90077-9
Türker, D. 2015. Contrasting instrumental views on corporate social responsibility: short-term versus long-term profit orientation approach, Procedia – Social and Behavioral Sciences 207: 568–576. https://doi.org/10.1016/j.sbspro.2015.10.128
Van Damme, E. 1991. Stability and Perfection of Nash Equilibria. Springer. 339 p. https://doi.org/10.1007/978-3-642-58242-4
Wang, X.; He, F.; Yang, H.; Gao, H. O. 2016. Pricing strategies for a taxi-hailing platform, Transportation Research Part E:
Logistics and Transportation Review 93: 212–231. https://doi.org/10.1016/j.tre.2016.05.011
Wang, Y.; Wang, S.; Wang, J.; Wei, J.; Wang, C. 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
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. https://doi.org/10.1016/j.techsoc.2016.06.004
Weibull, J. W. 1998. Evolution, rationality and equilibrium in games, European Economic Review 42(3–5): 641–649. https://doi.org/10.1016/S0014-2921(98)00012-9
Wirtz, J.; Tang, C. 2016. Uber: competing as market leader in the US versus being a distant second in China, in J. Wirtz, C. Lovelock (Eds.). Services Marketing: People, Technology, Strategy, 626–632. https://doi.org/10.1142/9781944659028_0019
Xiao, T. J.; Yu, G. 2006. Supply chain disruption management and evolutionarily stable strategies of retailers in the quantitysetting duopoly situation with homogeneous goods, European Journal of Operational Research 173(2): 648–668. https://doi.org/10.1016/j.ejor.2005.02.076
Yamauchi, A.; Tanimoto, J.; Hagishima, A.; Sagara, H. 2009. Dilemma game structure observed in traffic flow at a 2-to-1 lane junction, Physical Review E: Covering Statistical, Nonlinear, Biological, and Soft Matter Physics 79(3): 036104. https://doi.org/10.1103/PhysRevE.79.036104
Zha, L.; Yin, Y.; Du, Y. 2017. Surge pricing and labor supply in the ride-sourcing market, Transportation Research Procedia 23: 2–21. https://doi.org/10.1016/j.trpro.2017.05.002
Zha, L.; Yin, Y.; Yang, H. 2016. Economic analysis of ride-sourcing markets, Transportation Research Part C: Emerging Technologies 71: 249–266. https://doi.org/10.1016/j.trc.2016.07.010
Zhang, X. 2016. Research on legal attributes and limited license of “special car” services, Journal of Soochow University (Philosophy & Social Science Edition) (2): 80–90. (in Chinese).
Zhang, X.; Bao, H.; Skitmore, M. 2015. The land hoarding and land inspector dilemma in China: an evolutionary game theoretic perspective, Habitat International 46: 187–195. https://doi.org/10.1016/j.habitatint.2014.12.002
Anderson, D. N. 2014. “Not just a taxi”? For-profit ridesharing, driver strategies, and VMT, Transportation 41(5): 1099–1117. https://doi.org/10.1007/s11116-014-9531-8
Babu, S.; Mohan, U. 2018. An integrated approach to evaluating sustainability in supply chains using evolutionary game theory, Computers & Operations Research 89: 269–283. https://doi.org/10.1016/j.cor.2017.01.008
Barann, B.; Beverungen, D.; Müller, O. 2017. An open-data approach for quantifying the potential of taxi ridesharing, Decision Support Systems 99: 86–95. https://doi.org/10.1016/j.dss.2017.05.008
Bengtsson, N. 2015. Efficient informal trade: theory and experimental evidence from the Cape Town taxi market, Journal of Development Economics 115: 85–98. https://doi.org/10.1016/j.jdeveco.2015.02.003
Botsman, R.; Rogers, R. 2010. What’s Mine is Yours: the Rise of Collaborative Consumption. Harper Business. 304 p.
Caulfield, B. 2009. Estimating the environmental benefits of ridesharing: a case study of Dublin, Transportation Research Part D: Transport and Environment 14(7): 527–531. https://doi.org/10.1016/j.trd.2009.07.008
Cetin, T.; Deakin, E. 2019. Regulation of taxis and the rise of ridesharing, Transport Policy 76: 149–158. https://doi.org/10.1016/j.tranpol.2017.09.002
Chan, N. D.; Shaheen, S. A. 2012. Ridesharing in North America: past, present, and future, Transport Reviews 32(1): 93–112. https://doi.org/10.1080/01441647.2011.621557
Chen, Y.-F. 2017. The regulatory structure of “Internet plus”: a case study on “app-based ride and taxi services”, Jurist (1): 17–31 (in Chinese).
Chen, Z. 2015. Impact of Ride-Sourcing Services on Travel Habits and Transportation Planning. MSc Thesis. University of Pittsburgh, US. 64 p. Available from Internet: http://d-scholarship.pitt.edu/25827
Coninx, K.; Holvoet, T. 2015. Darwin in smart power grids – evolutionary game theory for analyzing self-organization in demand-side aggregation, in 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems, 21– 25 September 2015, Cambridge, MA, US, 101–110. https://doi.org/10.1109/SASO.2015.18
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. https://doi.org/10.1016/j.tra.2017.11.008
Cramer, J.; Krueger, A. B. 2016. Disruptive change in the taxi business: the case of Uber, American Economic Review 106(5): 177–182. https://doi.org/10.1257/aer.p20161002
Easley, D.; Kleinberg, J. 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press. https://doi.org/10.1017/CBO9780511761942
Davis, J. 2015. Drive at your own risk: Uber’s misrepresentations to UberX drivers about insurance coverage violate California’s unfair competition law, Boston College Law Review 56(3): 1097–1142.
Dong, Y.; Wang, S.; Li, L.; Zhang, Z. 2018. An empirical study on travel patterns of internet based ride-sharing, Transportation Research Part C: Emerging Technologies 86: 1–22. https://doi.org/10.1016/j.trc.2017.10.022
Ferguson, E. 1997. The rise and fall of the American carpool: 1970–1990, Transportation 24(4): 349–376. https://doi.org/10.1023/A:1004928012320
Fisher, R. A. 1930. The Genetical Theory of Natural Selection. Oxford University Press. 308 p.
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: 3756–3768. https://doi.org/10.1016/j.trpro.2017.05.232
Friedman, D. 1991. Evolutionary games in economics, Econometrica 59(3): 637–666. https://doi.org/10.2307/2938222
Fritz, C. 2014. Mobility-as-a-Service: Turning Transportation into a Software Industry. Available from Internet: https://venturebeat.com/2014/12/13/mobility-as-a-service-turningtransportation-into-a-software-industry
Glöss, M.; McGregor, M.; Brown, B. 2016. Designing for labour: Uber and the on-demand mobile workforce, in CHI’16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 7–12 May 2016, San Jose, California, US, 1632–1643. https://doi.org/10.1145/2858036.2858476
Harding, S.; Kandlikar, M.; Gulati, S. 2016. Taxi apps, regulation, and the market for taxi journeys, Transportation Research Part A: Policy and Practice 88: 15–25. https://doi.org/10.1016/j.tra.2016.03.009
Henao, A.; Marshall, W. 2017. A Framework for understanding the impacts of ridesourcing on transportation, in G. Meyer, S. Shaheen (Eds.). Disrupting Mobility: Impacts of Sharing Economy and Innovative Transportation on Cities, 197–209. https://doi.org/10.1007/978-3-319-51602-8_13
Herbert, A. 2016. Portlandia, ridesharing, and sex discrimination, Michigan Law Review Online 115: 18–25.
Hou, D.-H. 2015. Legitimacy and supervision system of internet private hire vehicles, Journal of University of Science and Technology Beijing (Social Sciences Edition) (6): 96–103. (in Chinese).
Hughes, R.; MacKenzie, D. 2016. Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators, Journal of Transport Geography 56: 36–44. https://doi.org/10.1016/j.jtrangeo.2016.08.014
Iwamura, Y.; Tanimoto, J. 2018. Complex traffic flow that allows as well as hampers lane-changing intrinsically contains socialdilemma structures, Journal of Statistical Mechanics: Theory and Experiment 2018(2): 023408. https://doi.org/10.1088/1742-5468/aaa8ff
Ji, P.; Ma, X.; Li, G. 2015. Developing green purchasing relationships for the manufacturing industry: an evolutionary game theory perspective, International Journal of Production Economics 166: 155–162. https://doi.org/10.1016/j.ijpe.2014.10.009
Jin, M.; Lei, X.; Du, J. 2010. Evolutionary game theory in multiobjective optimization problem, International Journal of Computational Intelligence Systems 3: 74–87. https://doi.org/10.1080/18756891.2010.9727754
Jin, S. T.; Kong, H.; Wu, R.; Sui, D. Z. 2018. Ridesourcing, the sharing economy, and the future of cities, Cities 76: 96–104. https://doi.org/10.1016/j.cities.2018.01.012
Kelley, K. L. 2007. Casual carpooling-enhanced, Journal of Public Transportation 10(4): 119–130. http://doi.org/10.5038/2375-0901.10.4.6
Kung, L.-C.; Liao, W.-H. 2018. An approximation algorithm for a competitive facility location problem with network effects, European Journal of Operational Research 267(1): 176–186. https://doi.org/10.1016/j.ejor.2017.11.037
Lewontin, R. C. 1961. Evolution and the theory of games, Journal of Theoretical Biology 1(3): 382–403. https://doi.org/10.1016/0022-5193(61)90038-8
Liu, D.; Xiao, X.; Li, H.; Wang, W. 2015a. Historical evolution and benefit–cost explanation of periodical fluctuation in coal mine safety supervision: an evolutionary game analysis framework, European Journal of Operational Research 243(3): 974–984. https://doi.org/10.1016/j.ejor.2014.12.046
Liu, H.; Zhang, F. 2015. The insurance dilemma in new online enabled transportation and legal countermeasures, Insurance Studies (12): 107–113. (in Chinese).
Liu, Q.; Li, X.; Hassall, M. 2015b. Evolutionary game analysis and stability control scenarios of coal mine safety inspection system in China based on system dynamics, Safety Science 80: 13–22. https://doi.org/10.1016/j.ssci.2015.07.005
Lobel, O. 2016. The law of the platform, in Social Science Research Network (SSRN) Electronic paper Collection, San Diego Legal Studies Paper No. 16-212. Available from Internet: https://ssrn.com/abstract=2742380
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. https://doi.org/10.1016/j.cie.2017.12.028
Minett, P.; Pearce, J. 2011. Estimating the energy consumption impact of casual carpooling, Energies 4(1): 126–139. https://doi.org/10.3390/en4010126
Morency, C. 2007. The ambivalence of ridesharing, Transportation 34(2): 239–253. https://doi.org/10.1007/s11116-006-9101-9
Nakata, M.; Yamauchi, A.; Tanimoto, J.; Hagishima, A. 2010. Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction, Physica A: Statistical Mechanics and its Applications 389(23): 5353–5361. https://doi.org/10.1016/j.physa.2010.08.005
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. https://doi.org/10.1073/pnas.36.1.48
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. https://doi.org/10.1016/j.trc.2017.03.017
Nisan, N.; Roughgarden, T.; Tardos, E.; Vazirani, V. V. 2007. Algorithmic Game Theory. Cambridge University Press. 778 p.
Ozkan-Canbolat, E.; Beraha, A.; Bas, A. 2016. Application of evolutionary game theory to strategic innovation, Procedia – Social and Behavioral Science 235: 685–693. https://doi.org/10.1016/j.sbspro.2016.11.069
Pal, R.; Scrimitore, M. 2016. Tacit collusion and market concentration under network effects, Economics Letters 145: 266–269. https://doi.org/10.1016/j.econlet.2016.07.005
Ramezani, M.; Nourinejad, M. 2017. Dynamic modeling and control of taxi services in large-scale urban networks: a macroscopic approach, Transportation Research Procedia 23: 41–60. https://doi.org/10.1016/j.trpro.2017.05.004
Rasouli, S.; Timmermans, H. 2014. Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research, Travel Behaviour and Society 1(3): 79–90. https://doi.org/10.1016/j.tbs.2013.12.001
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. https://doi.org/10.1016/j.tranpol.2015.10.004
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. https://doi.org/10.1016/j.plrev.2009.08.001
Rogers, B. 2015. The social costs of Uber, in Social Science Research Network (SSRN) Electronic paper Collection, Temple University Legal Studies Research Paper No. 2015-28. https://doi.org/10.2139/ssrn.2608017
Smith, J. M. 1982. Evolution and the Theory of Games. Cambridge University Press. 234 p.
Smith, J. M.; Price, G. R. 1973. The logic of animal conflict, Nature 246: 15–18. https://doi.org/10.1038/246015a0
Song, X.; Zhang, X. 2017. The cost benefit analysis in local government regulation on the car-hailing: illustrated by the case of Beijing, Journal of Chinese Academy of Governance (05): 123–130. (in Chinese).
Tan, J. 2016. Redefine Sharing: Sharing Practice of Uber (in China). Beijing: China Friendship Press. (in Chinese).
Tanimoto, J. 2015. Fundamentals of Evolutionary Game Theory and its Applications. Springer. 214 p. https://doi.org/10.1007/978-4-431-54962-8
Tanimoto, J.; Fujiki, T.; Wang, Z.; Hagishima, A.; Ikegaya, N. 2014a. Dangerous drivers foster social dilemma structures hidden behind a traffic flow with lane changes, Journal of Statistical Mechanics: Theory and Experiment 2014(11): P11027. https://doi.org/10.1088/1742-5468/2014/11/P11027
Tanimoto, J.; Kukida, S.; Hagishima, A. 2014b. Social dilemma structures hidden behind traffic flow with lane changes, Journal of Statistical Mechanics: Theory and Experiment 2014(7): P07019. https://doi.org/10.1088/1742-5468/2014/07/P07019
Tanimoto, J.; Nakamura, K. 2016. Social dilemma structure hidden behind traffic flow with route selection, Physica A: Statistical Mechanics and its Applications 459: 92–99. https://doi.org/10.1016/j.physa.2016.04.023
Tanimoto, J.; Sagara, H. 2011. Social diffusive impact analysis based on evolutionary computations for a novel car navigation system sharing individual information in urban traffic systems, Journal of Navigation 64(4): 711–725. https://doi.org/10.1017/S037346331100021X
Taylor, P. D.; Jonker, L. B. 1978. Evolutionary stable strategies and game dynamics, Mathematical Biosciences 40(1–2): 145–156. https://doi.org/10.1016/0025-5564(78)90077-9
Türker, D. 2015. Contrasting instrumental views on corporate social responsibility: short-term versus long-term profit orientation approach, Procedia – Social and Behavioral Sciences 207: 568–576. https://doi.org/10.1016/j.sbspro.2015.10.128
Van Damme, E. 1991. Stability and Perfection of Nash Equilibria. Springer. 339 p. https://doi.org/10.1007/978-3-642-58242-4
Wang, X.; He, F.; Yang, H.; Gao, H. O. 2016. Pricing strategies for a taxi-hailing platform, Transportation Research Part E:
Logistics and Transportation Review 93: 212–231. https://doi.org/10.1016/j.tre.2016.05.011
Wang, Y.; Wang, S.; Wang, J.; Wei, J.; Wang, C. 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
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. https://doi.org/10.1016/j.techsoc.2016.06.004
Weibull, J. W. 1998. Evolution, rationality and equilibrium in games, European Economic Review 42(3–5): 641–649. https://doi.org/10.1016/S0014-2921(98)00012-9
Wirtz, J.; Tang, C. 2016. Uber: competing as market leader in the US versus being a distant second in China, in J. Wirtz, C. Lovelock (Eds.). Services Marketing: People, Technology, Strategy, 626–632. https://doi.org/10.1142/9781944659028_0019
Xiao, T. J.; Yu, G. 2006. Supply chain disruption management and evolutionarily stable strategies of retailers in the quantitysetting duopoly situation with homogeneous goods, European Journal of Operational Research 173(2): 648–668. https://doi.org/10.1016/j.ejor.2005.02.076
Yamauchi, A.; Tanimoto, J.; Hagishima, A.; Sagara, H. 2009. Dilemma game structure observed in traffic flow at a 2-to-1 lane junction, Physical Review E: Covering Statistical, Nonlinear, Biological, and Soft Matter Physics 79(3): 036104. https://doi.org/10.1103/PhysRevE.79.036104
Zha, L.; Yin, Y.; Du, Y. 2017. Surge pricing and labor supply in the ride-sourcing market, Transportation Research Procedia 23: 2–21. https://doi.org/10.1016/j.trpro.2017.05.002
Zha, L.; Yin, Y.; Yang, H. 2016. Economic analysis of ride-sourcing markets, Transportation Research Part C: Emerging Technologies 71: 249–266. https://doi.org/10.1016/j.trc.2016.07.010
Zhang, X. 2016. Research on legal attributes and limited license of “special car” services, Journal of Soochow University (Philosophy & Social Science Edition) (2): 80–90. (in Chinese).
Zhang, X.; Bao, H.; Skitmore, M. 2015. The land hoarding and land inspector dilemma in China: an evolutionary game theoretic perspective, Habitat International 46: 187–195. https://doi.org/10.1016/j.habitatint.2014.12.002