Decision Support System for Integrated Corridor Management using Artificial Intelligence

During the past two decades, the Federal Highway Administration (FHWA) has invested heavily in researching, piloting, and demonstrating that Integrated Corridor Management (ICM) strategies and systems are a viable alternative to mitigating congestion when lane expansion is not possible. The vision of ICM is that transportation networks will realize significant improvements in the efficient movement of people and goods through institutional collaboration and aggressive, proactive integration of existing infrastructure along major corridors. Typically, an ICM system combines both arterial and freeway management components and consists of strategies such as dynamic lane control, speed harmonization, ramp metering, demand management, traffic signal coordination and similar strategies.

The Tennessee Department of Transportation (TDOT) is currently in the deployment stages of a multiphase project called the I-24 Smart Corridor. This project represents a $95M dollar investment to develop, implement, and deploy a comprehensive systems management strategy to create a balanced, responsive, and equitable system. The I-24 Smart Corridor is a 28 mile corridor consisting of Interstate 24 with its corresponding major arterial, State Route 1 (Murfreesboro Pike). TDOT, working in concert with the four municipalities of Nashville, La Verne, Smyrna, and Murfreesboro, has determined that further expansions to I-24 and SR 1 are not possible. However, a set of ICM strategies has been developed and is currently being implemented in phases along this corridor. These strategies are expected to reduce crashes by 6- 7% and reduce the travel time index (TTI) by 8-9%, among other improvements. The safety benefits from these strategies are estimated at $3.8 million, and the mobility benefits at $14.4 million. The next phase of this project is the development of a central software system and associated Decision Support System (DSS). The ICM DSS is the tool utilized by operations staff to evaluate and recommend traffic management strategies in real time for activation on the corridor based on the current conditions. In this proposal, TDOT and the project team are proposing to expedite and reduce the implementation and maintenance cost of this ICM DSS by developing and utilizing an Artificial Intelligence (AI) based system as opposed to the current state of practice. Once successful, this will provide other DOTs with a roadmap for implementation of similar ICM corridors at an accelerated pace while maximizing the return on investment.

Press release:

USDOT Announcement:

U.S. Department of Transportation
Lead PI
Dan Work
Janos Sztipanovits