Coping with real-world challenges in real-time urban traffic control

Abstract

In urban road networks, the use of real-time adaptive traffic signal control systems faces two typical challenges. First, various sources of uncertainty and disturbance can significantly degrade the accuracy of real-time flow predictions. Second, the optimization of vehicle flows must also give active attention to other transportation modes such as bus transit and pedestrian flows. In this paper, these challenges are investigated in the context of a recently implemented system called SURTRAC (Scalable URban TRAffic Control), which has now been running continuously in an actual urban environment for more than a year. SURTRAC takes a decentralized, schedule-driven approach to real-time traffic control and its design aims at urban (grid-like) networks with multiple, competing dominant flows that shift through the day. Motivated by observations of the system in operation, several strategies are proposed for strengthening the basic SURTRAC algorithm to better deal with real-world uncertainties and disruptive events, as well as multi-modal traffic demands. We evaluate the effectiveness of these strategies using both simulations and analysis of data collected from the pilot deployment.