Toward Large-Scale Agent Guidance in an Urban Taxi Service

TL;DR

This paper proposes intelligent agent guidance for urban taxis to reduce resource wastage caused by greedy cruising, using multiagent planning models to improve efficiency.

Contribution

It introduces two novel models for taxi guidance—one assuming fully cooperative drivers and another with rational drivers seeking profit maximization.

Findings

Both models significantly improve taxi service efficiency.

The cooperative model enables systemwide optimal cruising policies.

The game-theoretic model finds Nash equilibrium strategies for drivers.

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.