Modeling taxi driver anticipatory behavior

Authored by Harry Timmermans, Zhong Zheng, Soora Rasouli

Date Published: 2018

DOI: 10.1016/j.compenvurbsys.2018.01.008

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

As part of a wider behavioral agent-based model that simulates taxi drivers' dynamic passenger-finding behavior under uncertainty, we preterit a model of strategic behavior of taxi drivers in anticipation of substantial time varying demand at locations such as airports and major train stations. The model assumes that, considering a particular decision horizon, a taxi driver decides to transfer to such a destination based on a reward function. The dynamic uncertainty of demand is captured by a time dependent pick-up probability, which is a cumulative distribution function of waiting time. The model allows for information learning by which taxi drivers update their beliefs from past experiences. A simulation on a real road network, applied to test the model, indicates that the formulated model dynamically improves passenger-finding strategies at the airport. Taxi drivers learn when to transfer to the airport in anticipation of the time-varying demand at the airport to minimize their waiting time.

Tags

Uncertainty networks Network mobility Services Taxi behavior Passenger-finding strategies Dynamic learning