Traffic Volume Estimate Based on Low Penetration Connected Vehicle Data at Signalized Intersections: A Bayesian Deduction Approach

The emergence of connected vehicle (CV) technologies has created new traffic control opportunities, among them, is the potential to estimate volume without approach lane detection. Rather than requiring the expense and effort to install and maintain detector systems, this new ``detector-free'' method permits traffic volume to be estimated from CV GPS trajectory data. Unfortunately, however, CV GPS methods are limited not only to locations where CV GPS data can be recorded, but also limited to time when CV GPS data is recorded. The goal of this research was to overcome these limitations and permit volume estimation to be accomplished under any location or condition, including low-penetration CV environments. The contributions made by this work are significant in two respects. First, it creates an improved queue-based method to estimate intersection approach volumes during each signal cycle with sparse CV data. Second, the research demonstrates the application of a Bayesian deduction method to approximate volume with no CV trajectory data. To accomplish this, traffic volumes are assumed to be time-dependent Poisson distributed throughout the day, and CV data were used to estimate CV volume and further set as prior to deduce the time-dependent Poisson arrival rate. To verify and evaluate the accuracy and effectiveness of this new method under a range of potential traffic conditions, a simulation case study and a NGSIM case study were implemented. Results of both case studies resulted in estimated-to-actual arrival rate average errors as low as 4.2 percent and volume estimation errors as low as 0.9 percent.