AI

Approximating the Effects of Installed Traffic Lights: A Behaviorist Approach Based on Travel Tracks

Abstract

Decades of research have been directed towards improving the timing of existing traffic lights. In many parts of the world where this research has been conducted, detailed maps of the streets and the precise locations of the traffic lights are publicly available. Continued timing research has recently been further spurred by the increasing ubiquity of personal cell-phone based GPS systems. Through their use, an enormous amount of travel tracks have been amassed — thus providing an easy source of real traffic data. Nonetheless, one fundamental piece of information remains absent that limits the quantification of the benefits of new approaches: the existing traffic light schedules and traffic light response behaviors. Unfortunately, deployed traffic light schedules are often not known. Rarely are they kept in a central database, and even when they are, they are often not easily obtainable. The alternative, manual inspection of a system of multiple traffic lights may be prohibitively expensive and time-consuming for many experimenters. Without the existing light schedules, it is difficult to ascertain the real-improvements that new traffic light algorithms and approaches will have — especially on traffic patterns that have not yet been encountered in the collected data. To alleviate this problem, we present an approach to estimating existing traffic light schedules based on collected GPS-travel tracks. We present numerous ways to test the results and comprehensively demonstrate them on both synthetic and real data. One of the many uses, beyond studying the effects of existing lights in previously unencountered traffic flow environments, is to serve as a realistic baseline for light timing and schedule optimization studies.