Modeling the Traffic Impacts from Automated and Connected Vehicles in a Complex, Congested Urban Setting
Corresponding Author: Jeff Shelton, Texas A&M Transportation Institute
Presented By: Jeff Shelton, Texas A&M Transportation Institute
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 at the Texas A&M Transportation Institute (TTI) used traffic modeling software to develop and test a vehicle mimicking the behaviors of several automated and connected vehicle applications in a congested and complex urban network. The vehicle design drew inspiration from several sources, including cooperative adaptive cruise control, queue warning, and speed harmonization. However, the cooperative adaptive cruise control algorithms used in the model may not align with expected implementations and conceptual models. To verify that the custom vehicle behaved as expected, the team tested it against results from previous studies modeling similar technologies.
The team was able to replicate these results, where a simplified single-lane network full of equipped vehicles roughly doubled the normal highway capacity (increasing from about 2,000 vehicles/hour/lane [veh/hr/ln] to nearly 4,000 veh/hr/ln). This result indicated that the custom-coded vehicle was functioning properly and could be tested in a more complex and realistic setting. The TTI team then used a multi-resolution model (MRM) that combines aspects from three types of modeling: macroscopic, mesoscopic, and microscopic. This type of modeling requires more steps and is more time intensive, but the result more closely approximates real-world conditions in a few important ways. This process incorporates and integrates data from several different sources—such as local sociodemographic information—which results in a model populated with routes that more accurately reflect the number of trips Austinites might take, their length and destination, and how they travel (mode). The MRM also provides a more robust approach for analyzing large sections of freeway corridors (e.g., speeds and travel times) and the effects of CVs on the freeway.