Modeling taxi driver dynamic passenger-finding behavior under uncertainty

In many cities, taxis continuously circulate in search of customers. Such dynamic search behavior consumes much road space, contributing to local traffic congestion and air pollution. To better understand movements of vacant taxis, several studies have examined taxi drivers' movement patterns. However, topics such as dynamic passenger finding strategies, endogenous taxi travel demand and drivers learning processes have still been scarcely addressed. This article proposes a model to simulate taxi drivers' dynamic passenger search behavior under uncertainty. The model emphasizes: (i) taxi drivers' subjective utility of passenger finding strategies under uncertainty, (ii) information learning and updating processes, and (iii) the sensitivity to dynamic passengers demand.