Saldivar-Carranza, E. D., Li, H., Mathew, J. K., Desai, J., Platte, T., Gayen, S., Sturdevant, J., Taylor, M., Fisher, C., & Bullock, D. M. (2023). Next generation traffic signal performance measures: Leveraging connected vehicle data. West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284317625
Date of this Version
performance measures, transportation engineering, traffic engineering, traffic control, traffic signals, connected vehicle, trajectory data, event data
High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders.