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|Title:||A POMDP model for guiding taxi cruising in a congested urban city|
|Authors:||Agussurja, L. |
|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)|
|Appears in Collections:||Staff Publications|
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