A System Dynamics Model for Safety Supervision of Online Car-Hailing From an Evolutionary Game Theory Perspective

Abstract

Car-hailing safety supervision is of great significance to ease the pressure on urban public transportation and facilitate people to travel safely and conveniently. In this paper, a novel tripartite evolutionary game theory is proposed to describe the interaction mechanism of the government supervision department, online vehicle platform security monitoring department, and car sharing owner in the process of China's Internet ride-hailing operation. The replication dynamics equations are used to elaborate the evolutionary stable strategies of stakeholders and system dynamics are presented to explore the dynamic simulation process of the evolutionary game model, analyze the stability of stakeholder interactions and determines an equilibrium solution. The meaningful simulation results are as follows: there is no stable strategy for the evolution of the three-party selection strategy; the optimized dynamic penalty incentive control scenario can not only effectively suppress fluctuations, but also achieve the effect of obtaining an ideal evolutionary stable strategy. It shows that the cost of government supervision, the platform monitoring and the online hailed car owner can influence the strategy choice of the stakeholders; the government should impose appropriate fines and penalty on the platform and reward car owners, which will help all parties to the game reach a stable state; appropriate punishment-reward factors help the system to reach steady state more easily. These results can provide a theoretical guidance for the government to promote the development of online car-hailing service and establishment of the supervision and management system. INDEX TERMS Online car-hailing, safety supervision and monitoring, evolutionary game theory, dynamic penalty incentive control scenario, government intervention.

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