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

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