A POMDP Model for Guiding Taxi Cruising in a Congested Urban City

Abstract

We consider a partially observable Markov decision process (POMDP) model for improving a taxi agent cruising decision in a congested urban city. Using real-world data provided by a large taxi company in Singapore as a guide, we derive the state transition function of the POMDP. Specifically, we model the cruising behavior of the drivers as continuous-time Markov chains. We then apply dynamic programming algorithm for finding the optimal policy of the driver agent. Using a simulation, we show that this policy is significantly better than a greedy policy in congested road network.

DOI: 10.1007/978-3-642-25324-9_36

Extracted Key Phrases

5 Figures and Tables

Cite this paper

@inproceedings{Agussurja2011APM, title={A POMDP Model for Guiding Taxi Cruising in a Congested Urban City}, author={Lucas Agussurja and Hoong Chuin Lau}, booktitle={MICAI}, year={2011} }