Impacts of connected vehicles in a complex, congested urban freeway setting using multi-resolution modeling methods

Abstract

Determining the effect automated and connected vehicles could have on traffic flow would – ideally – require testing the vehicles themselves in a real-world environment. In the absence of large-scale, real-world testing, researchers used traffic modeling software to develop and test a vehicle mimicking the behaviors of several automated and connected vehicle (CV) applications in a congested and complex urban network. The algorithm behind the CV ran a suite of mobility-focused applications, inspired by cooperative adaptive cruise control (CACC), speed harmonization, and queue warning applications. The CV was first tested on a small sample network, consistent with approaches obtained from a review of the literature. The research team then sought to understand the potential effects of CV technology on congestion and mobility in a DTATexas context by modeling the traffic impacts of CVs at varying market penetrations on a twelve-mile section of I-35 in Austin, running from south of Riverside Dr. to Parmer Ln at 2035 population levels. Researchers used a multi-resolution modeling (MRM) methodology, which incorporates macroscopic, mesoscopic, and microscopic models.