Fortran and McMaster University jointly developed a Real-Time Adaptive Traffic Signal Control using Connected Vehicle Data

Fortran and McMaster University researchers jointly developed an adaptive control algorithm to control traffic signal in real-time. Conventional signal control systems use pre‐determined signal timing schedules which often contribute to traffic congestion and delay. Adaptive signal control technologies adjust the traffic lights to accommodate traffic flow patterns, enable the use of maximum capacity of infrastructure and reduce costs for both motorists and operating agencies. Adaptive signal control technologies can use real time Vehicle‐to‐Vehicle (V2V) and Vehicle‐to‐Infrastructure (V2I) data to determine the traffic volume and adjust the traffic light timing. This project aimed at developing an adaptive signal control system using connected vehicle data. The simulation results proved improvement in performance despite low penetration rate of Connected vehicle in the market (<10%). Fortran plans to integrate the developed algorithm in ARIA Advance Transportation Management System (ATMS). ARIA is Fortran's next generation web-based and open platform ATMS. The centerpiece component of Aria is a reliable, comprehensive, integrated real-time centralized Traffic Signal Control System. It provides an integrated platform for ITS initiatives including traffic signal control, information management, and graphical data display and manipulation. The project was supported by Ontario Centers of Excellence (OCE) and Natural Sciences and Engineering Research Council of Canada (NSERC).