Posted April 05, 2021
March 30, 2021 – Southwest Research Institute, in collaboration with Vanderbilt University, is developing machine learning algorithms to help the Tennessee Department of Transportation (TDOT) coordinate traffic management and incident response along portions of Interstate 24 in the rapidly growing Nashville region.
The project will use artificial intelligence to enhance an integrated corridor management (ICM) system, using software and systems to promote smart mobility and improve collaboration among various transportation agencies.
“SwRI’s ICM solutions fuse data across freeways, surface streets and transit systems to help balance traffic flow and improve performance of the entire corridor,” said Samantha Blaisdell, a program manager at SwRI.
SwRI’s Intelligent Systems Division and Vanderbilt University will develop an Artificial Intelligence-based ICM Decision Support System (DSS) through a TDOT grant funded by the U.S. Department of Transportation.
Integrated corridor management is making its way out of the laboratory and hitting the road following two decades of research led by the Federal Highway Administration (FHWA). ICM systems manage freeways and arterial roadways with dynamic lane control, speed harmonization, traffic signal control, ramp metering, demand management and other strategies. Deployment, however, has been limited by reliance on conventional traffic simulation modeling, which can be cost prohibitive due to the time and resources required to develop and maintain traffic models.
The project will use artificial intelligence in the place of simulation models to learn from and mimic operator behavior and decision making. This will enable quicker accident response and mitigation, rerouting traffic around problem areas quickly and efficiently, and ensuring state and local agency collaboration.
“SwRI’s TDOT research aims to overcome the roadblocks of ICM traffic modeling by using artificial intelligence algorithms to speed up the analysis of traffic,” said Clay Weston, an SwRI project manager leading the project. “After training the system using traffic patterns, the algorithms will be able to recommend alternative routes in real-time, taking advantage of high-capacity urban roads and surface streets.”
The SwRI-led decision tool will have several applications, such as traffic signal coordination on underutilized roads to ease congestion on highways. State transportation operations staff will use the decision tool to evaluate and recommend traffic management strategies for real-time diversion routing. Using a web interface, the DSS will integrate into the state’s management center, the public agency owned ActiveITS™ and other regional intelligent transportation systems (ITS).