History-based traffic control

ABSTRACT

What if traffic lights gave you a break after you've spent a long time waiting in traffic elsewhere? In this paper we examine a variety of multi-agent traffic light controllers which consider vehicles' past stopped-at-red histories. For example, a controller might distribute credits to cars as they wait and award the green light to lanes with the most credits, allowing cars to keep the credits they accumulate during travel. Such history-based controllers are intended to provide a kind of global fairness, reducing the variance in mean time spent waiting at lights during trips. We compare these controllers against other multi-agent controllers which only consider present information, and discover, among other things, that while the history-based controllers are among the most robust, they often unexpectedly provide more efficiency than fairness.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms

Keywords:
efficiency-fairness tradeoff, fairness, traffic light controller, urban traffic control