Conference Proceeding Article
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.
agent application, intelligent transportation, POMDP, taxi service
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Transportation
Intelligent Systems and Decision Analytics
Advances in Artificial Intelligence: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4: Proceedings
City or Country
AGUSSURJA, Lucas and LAU, Hoong Chuin.
A POMDP model for guiding taxi cruising in a congested urban city. (2011). Advances in Artificial Intelligence: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4: Proceedings. 7094, 415-428. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1385
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.