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. © 2011 Springer-Verlag.
CITATION STYLE
Agussurja, L., & Lau, H. C. (2011). A POMDP model for guiding taxi cruising in a congested urban city. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7094 LNAI, pp. 415–428). https://doi.org/10.1007/978-3-642-25324-9_36
Mendeley helps you to discover research relevant for your work.