Estimation of measures of effectiveness based on Connected Vehicle data

Abstract

Vehicle-infrastructure cooperation via the Connected Vehicle initiative is a promising mobile data source for improving real-time traffic management applications such as adaptive signal control. This paper focuses on developing estimation methods with the use of Connected Vehicle data for several measures of effectiveness (e.g., queue length, average speed, number of stops), essential for determining traffic conditions on urban signalized arterials for real-time applications. This research systematically determines minimum penetration rates that allow accurate estimates for a wide range of measures of effectiveness in undersaturated traffic conditions. The estimation of these measures and minimum penetration requirements has been tested using Next Generation Simulation (NGSIM) data.