Aligning social welfare and agent preferences to alleviate traffic congestion

Abstract

Multiagent coordination algorithms provide unique insights into the challenging problem of alleviating traffic congestion. What is particularly interesting in this class of problem is that no individual action (e.g., leave at a given time) is intrinsically bad but that combinations of actions among agents lead to undesirable outcomes. As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of actions. In general, the traffic problem can be approached from two distinct perspectives: (i) from a city manager's point of view, where the aim is to optimize a city wide objective function (e.g., minimize total city wide delays), and (ii) from the individual driver's point of view, where each driver is aiming to optimize a personal objective function (e.g., a function that minimizes the difference desired and actual arrival times at a destination). In many cases, these two objective functions are at odds with one another, where drivers aiming to optimize their own objectives yield to congestion and poor values of city objective functions.In this paper we present an objective shaping approach to both types of problems and study the system behavior that arises from the drivers' choices. We first show a topdown approach that provides incentives to drivers and leads to good values of the city manager's objective function. We then present a bottom-up approach that shows that drivers aiming to optimize their own personal timeliness objective lead to poor performance with respect to a city manager's objective function. Finally, we present the intriguing result that drivers that aim to optimize a modified version of their own timeliness function not only perform well in terms of the city manager's objective function, but also perform better with respect to their own original timeliness functions.

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