Automatically Assessing the Need for Traffic Signal Retiming Using Connected Vehicle Data

Abstract

Traffic signal controllers are a critical element in managing urban traffic systems effectively. The emergence of Automated Traffic Signal Performance Measures (ATSPM), aided by recent technological advancements, allows for continuous traffic performance monitoring, and supports traffic agencies in taking proactive measures. High-resolution trajectory data from connected vehicles (CVs) has surfaced as a cost-effective method for assessing ATSPM. Although various metrics have been developed to measure traffic signal performance, none have been specifically designed to predict the benefits of signal retiming. This study devises a novel metric using CV data to estimate the potential reduction in overall intersection delay that could result from signal retiming. Hence, this measure uniquely estimates the potential avoidable delay rather than simply the observed signal performance. This new metric could enable traffic agencies to predict the benefits of potential signal retiming without the need for conducting costly traffic surveys. Such a tool would help these agencies prioritize locations and times of day for signal retiming. This study outlines the process of calculating this index and employs the VISSIM microsimulation software to demonstrate and evaluate the index under various traffic scenarios and CV market penetration rates. In our experiments, the suggested metric successfully detected signal retiming needs in situations involving an imbalanced degree of saturation, traffic demand fluctuations on competing movements, and changes in traffic direction, even with CV penetration rates as low as 10%.