Real-Time Estimation of Saturation flow Rates for Dynamic Traffic Signal Control using Connected-Vehicle Data

Abstract

Existing adaptive traffic signal control (ATSC) systems rely on dedicated fixed-point sensors, such as inductive loop detectors or video cameras, for measuring traffic demands and discharge saturation flow rates. The cost associated with installation, operation, and maintenance of these sensors is one of the factors that limit the deployment of ATSCs. The emergence of connected vehicles (CVs), which continuously broadcast their speed, position, heading, and other information to other vehicles and to roadside infrastructure, provides an opportunity to reduce the reliance of ATSC on data from fixed sensors and potentially to reduce ATSC deployment costs. However, so that existing ATSC systems can operate by using CV data, a methodology is needed for estimating demands and saturation flow rate based on CV data instead of fixed sensor data. This paper focuses on estimating the time-varying saturation flow rate for individual lane groups at signalized intersections solely on the basis of CV data. The accuracy of a proposed methodology is quantified through microsimulation for a range of traffic conditions, lane group configurations, and levels of market penetration (LMP) of CVs. The analysis shows that the proposed methodology can capture temporal variations in the saturation flow rate caused by road incidents, queues spilling back from downstream bottlenecks, and lane closures. The evaluation results show that the mean absolute relative error of the lane group saturation flow rate ranged from approximately 2% to 9% when LMP = 20% and only 1% to 2% when LMP = 100%.