A POMDP Model for Guiding Taxi Cruising in a Congested Urban City

  • Lucas Agussurja
  • Hoong Chuin Lau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


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 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lucas Agussurja
    • 1
  • Hoong Chuin Lau
    • 2
  1. 1.The Logistics Institute Asia PacificNational University of SingaporeSingapore
  2. 2.School of Information SystemsSingapore Management UniversitySingapore

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