Taxi in competition with online car-hailing drivers: Policy implication to operating strategies - Tianqi Gu; Weiping Xu; Peijie Shi; Ruiyi Wang; Inhi Kim (Multimodal Transportation, 2024)
Car-hailing and taxis coexist and constitute a healthy market in normal times when demand is sufficient for growing supplies. However, in a limited market influenced by disruptive issues such as COVID-19, drivers from online car-hailing and local taxi operators have been compelled to engage in competition due to the shrinking revenue. The distinct occupational characteristics and operation patterns of drivers in different groups directly influence their operational strategies (whether to operate or not), which remains an unexplored research area. To this end, this article analyzes the contrast in diverse operating indicators between the two service models before and following the outbreak of the epidemic based on a local case study in Suzhou. It establishes an income matrix for drivers in varied scenarios and employs evolutionary game theory (EGT) to dissect the dynamic operating strategies of taxi and online car-hailing drivers. Furthermore, considering the impact of disruptive issues on market demand, this study also introduces an optimized dynamic income incentive mechanism. The findings demonstrate that when disruptive issues arise and last for a considerable extended period, a 'winner-takes-all' market scenario might unfold - the potential monopoly of one service type. To circumvent this scenario, proactive human intervention can be employed at opportune moments, such as augmenting initial income, to establish the equilibrium state of ESS (1,1)—a balanced and robust coexistence of the two services. Overall, this paper provides a set of novel indicators to identify different drivers’ operation strategies, and applies EGT to analyze and estimate their operation strategies during disruptive events.