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


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.

DOI: 10.1007/978-3-642-25324-9_36

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Modeling urban taxi services with multiple user classes and vehicle modes

  • K I Wong, S C Wong, H Yang, J H Wu
  • 2008

Modeling the bilateral microsearching behavior for urban taxi services using the absorbing markov chain approach

  • K I Wong, S C Wong, M G H Bell, H Yang
  • 2005

Demand-supply equilibrium of taxi services in a network under competition and regulation

  • H Yang, S C Wong, K I Wong
  • 2002

Calibration and validation of network equilibrium taxi model for hong kong

  • K I Wong, S C Wong, H Yang
  • 1999

A network model of urban taxi services

  • H Yang, S C Wong
  • 1998
Showing 1-2 of 2 extracted citations