Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-25324-9_36
Title: A POMDP model for guiding taxi cruising in a congested urban city
Authors: Agussurja, L. 
Lau, H.C.
Keywords: agent application
intelligent transportation
POMDP
taxi service
Issue Date: 2011
Citation: Agussurja, L.,Lau, H.C. (2011). A POMDP model for guiding taxi cruising in a congested urban city. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7094 LNAI (PART 1) : 415-428. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-25324-9_36
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. © 2011 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/112996
ISBN: 9783642253232
ISSN: 03029743
DOI: 10.1007/978-3-642-25324-9_36
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