Strategic game between on-demand operators with incomplete demand information

Abstract

This study investigates the strategic interactions between competing transport network companies (TNCs) within a multi-modal transportation system at both operational and informational levels. A bi-level model is developed, where at the upper level, each TNC independently optimizes its trip fare to maximize profit, and at the lower level, travelers select among available services according to a multinomial logit (MNL) model. To better capture real-world uncertainty, the model incorporates an incomplete-information setting, where TNCs do not have exact knowledge of travelers’ value of in-vehicle time (VTT), value of waiting time (VWT), and the total demand. Instead, they must learn and iteratively update their beliefs based on observed outcomes, following a Bayesian framework. Numerical experiments demonstrate that the market size plays a decisive role in the accuracy of information inference. The benefits of ignorance are also shown in our results, that less information may help to mitigate the intensity of competition.