A POMDP model for guiding taxi cruising in a congested urban city

Authors:
Lucas Agussurja;Hoong Chuin Lau
Affiliations:
The Logistics Institute Asia Pacific, National University of Singapore, Singapore;School of Information Systems, Singapore Management University, Singapore
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

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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.