01660378

Component

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

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values.

18-02520

English

15p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References

Behavior; Decision making; Mathematical models; Passengers; Taxicab drivers; Waiting time

Highways; Passenger Transportation; Planning and Forecasting

Transportation Research Board Annual Meeting 2018 Paper #18-02520

TRIS, TRB, ATRI

Jan 8 2018 10:36AM