Uncertainty in Artificial Intelligence
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Toward Large-Scale Agent Guidance in an Urban Taxi Service
Lucas Agussurja, Hoong Chuin Lau
Empty taxi cruising represents a wastage of resources in the context of urban taxi services. In this work, we seek to minimize such wastage. An analysis of a large trace of taxi operations reveals that the services' inefficiency is caused by drivers' greedy cruising behavior. We model the existing system as a continuous time Markov chain. To address the problem, we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers. Then, drawing from AI literature on multiagent planning, we explore two possible ways to compute such guidance. The first formulation assumes fully cooperative drivers. This allows us, in principle, to compute systemwide optimal cruising policy. This is modeled as a Markov decision process. The second formulation assumes rational drivers, seeking to maximize their own profit. This is modeled as a stochastic congestion game, a specialization of stochastic games. Nash equilibrium policy is proposed as the solution to the game, where no driver has the incentive to singly deviate from it. Empirical result shows that both formulations improve the efficiency of the service significantly.
Pages: 36-43
PS Link:
PDF Link: /papers/12/p36-agussurja.pdf
AUTHOR = "Lucas Agussurja and Hoong Chuin Lau",
TITLE = "Toward Large-Scale Agent Guidance in an Urban Taxi Service",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "36--43"

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