Connected vehicle penetration rate for estimation of arterial measures of effectiveness. (November 2015)

• Record Type: Journal Article
• Authors: Argote-Cabañero, Juan; Christofa, Eleni; Skabardonis, Alexander
• Is Part Of: Transportation research. Volume 60(2015)
• Page Start: 298
• Page End: 312
• Publication Date: 2015-11
• Subjects: Connected vehicles -- Measures of effectiveness -- Minimum penetration rate -- Signalized arterials -- Traffic estimation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.08.013
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Estimation of Measures of Effectiveness (MOEs) using Connected Vehicle (CV) data. Method to estimate minimum CV penetration rate for accurate MOE estimation. Understanding how traffic flow levels impact minimum penetration rate requirements. Minimum CV rate depends on accuracy, MOE variability, traffic, and signal settings. Minimum penetration can be predicted within 1% for most MOEs and demand levels. Abstract: The Connected Vehicle (CV) technology is a mobile platform that enables a new dimension of data exchange among vehicles and between vehicles and infrastructure. This data source could improve the estimation of Measures of Effectiveness (MOEs) for traffic operations in real-time, allowing to perfectly monitor traffic states after being fully adopted. However, as with any novel technology, the CV adoption will be a gradual process. This research focuses on determining minimum CV technology penetration rates that would guarantee accurate MOE estimates on signalized arterials. First, we present estimation methods for various MOEs such as average speed, number of stops, acceleration noise, and delay, followed by an initial assessment of the penetration rates required to accurately estimate them in undersaturated and oversaturated conditions. Next, we propose a methodology to determine the minimum CV market penetration rates to guarantee accurate MOE estimates as a function of traffic conditions, signal settings, sampling duration, and the MOE variability. A correction factor is also provided to account for small vehicle populations where sampling is done without replacement. The methodology is tested in a simulated segment of the San Pablo Avenue arterial in Berkeley, CA. The outcomes show that the minimum penetration rate required can be estimated within 1% for most MOEs under a wide range of traffic conditions. The proposed methodology can be used to determine if MOE estimates obtained with a portion of CV equipped vehicles can yield accurate enough results. The methodology could also be used to develop and assess control strategies towards improved arterial traffic operations.