AI

Micro-Auction-Based Traffic-Light Control: Responsive, Local Decision Making

Abstract

Real-time, responsive optimization of traffic flow serves to address important practical problems: reducing drivers’ wasted time and improving city-wide efficiency, as well as reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of basic traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive to current traffic situations within the existing, deployed, infrastructure. In this paper, we use micro-auctions as the organizing principle with which to incorporate local induction loop information; no other outside sources of information are assumed. At every time step in which a phase change is permitted, each light conducts a decentralized, weighted, micro-auction to determine which phase to instantiate next. We test the lights on real-world data collected over a period of several weeks around the Mountain View, California area. In our simulations, the auction mechanisms based only on local sensor data surpass longer-term planning approaches that rely on widely placed sensors and communications.