Automatic Signal Retiming for Large Scale Networks with Vehicle Trajectory Data
Principal Investigator(s):
Henry Liu, Director – Center for Connected and Automated Transportation (CCAT)
Director – Mcity
Professor of Civil and Environmental Engineering – The University of Michigan
Research Professor – The University of Michigan Transportation Research Institute
Project Abstract:
Traffic signal optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of detection systems, most intersections in practice are controlled by fixed-time traffic signals that rely on manual data collection and are not regularly optimized. Readily available vehicle trajectory data offers unprecedented opportunities for a more efficient use of existing infrastructure and resources. Our recently developed OSaaS (Optimizing Signals as a Service) system uses vehicle trajectory data as the only input to optimize traffic signals. OSaaS allows us to easily monitor traffic performance, diagnose signal timing issues, and optimize signal timing parameters. However, to put OSaaS into practice, an automated process needs to be developed so that manual effort can be minimized. For example, traffic flow parameters such as saturation flow rate and free-flow speed can be calibrated automatically by using historical vehicle trajectory data. Therefore, the project will further develop OSaaS into a data-driven automatic signal retiming system that will update signal timing parameters for fixed-time and coordinated-actuated signalized intersections on an iterative basis (i.e., bi-weekly, monthly).
Institution(s): University of Michigan Transportation Research Institute
Award Year: 2024
Research Focus: Mobility
Project Form(s):