Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

Document Type


Publication Date



34 pp.


For traffic signal control, Time of Day (TOD) mode of operations is widely deployed in practice for selecting a signal timing plan. However, TOD mode in not effective in adapting to variations in traffic conditions, such as special events and holidays, incidents, etc. Several research studies have reported the potential of Traffic Responsive Control operation or Traffic Responsive Plan Selection (TRPS) in reducing delays and the number of stops. For successful implementation of TRPS, accurate traffic state estimation is essential. The current study in this direction investigates a methodology for traffic state estimation for a corridor in Morgantown, WV, by using system detector data and connected vehicles (CV) data. Data from CVs form the basis to estimate queue lengths at signalized intersection approaches. While using data from multiple sources, a single measure in terms of three plan selection parameter was obtained, based on which discriminant functions were developed to classify the observations into states. Based on kmeans clustering, similar traffic states were grouped together and a new set of states were suggested in place of the original states for which up to 93% classification accuracy was obtained. Overall, it was demonstrated that queue length data can be a valuable source of information for traffic state estimation that is needed for implementing the TRPS framework.


"No restrictions. This document is available from the National Technical Information Service, Springfield, VA 22161."


0000-0003-2134-2133 (Salahshour), 0000-0003-2003-9343 (Cetin)

Original Publication Citation

Fulari, S., Abbas, M. M., Salahshour, B., Cetin, M., Zatar, W., & Nichols, A. P. (2019). Leveraging connected vehicles to enhance traffic responsive traffic signal control. US Department of Transportation. https://rosap.ntl.bts.gov/view/dot/42020/dot_42020_DS1.pdf