A POMDP model for guiding taxi cruising in a congested urban city

• Main Authors: AGUSSURJA, Lucas, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: agent application; intelligent transportation; POMDP; taxi service; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/1385; https://ink.library.smu.edu.sg/context/sis_research/article/2384/viewcontent/pomdpTaxi_2011_MICAI.pdf
• Institution: Singapore Management University

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.