Toward Large-Scale Agent Guidance in an Urban Taxi Service

Lucas Agussurja, Hoong Chuin Lau
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:33-40, 2012.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-agussurja12a, title = {Toward Large-Scale Agent Guidance in an Urban Taxi Service}, author = {Agussurja, Lucas and Lau, Hoong Chuin}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {33--40}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/agussurja12a/agussurja12a.pdf}, url = {https://proceedings.mlr.press/r10/agussurja12a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Toward Large-Scale Agent Guidance in an Urban Taxi Service %A Lucas Agussurja %A Hoong Chuin Lau %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-agussurja12a %I PMLR %P 33--40 %U https://proceedings.mlr.press/r10/agussurja12a.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Agussurja, L. & Lau, H.C.. (2012). Toward Large-Scale Agent Guidance in an Urban Taxi Service. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:33-40 Available from https://proceedings.mlr.press/r10/agussurja12a.html. Reissued by PMLR on 04 October 2026.

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