Project Title

Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

Collaborating Universities

Marshall University
One John Marshall Drive
Huntington, WV 25755

Old Dominion University
1 Old Dominion University
Norfolk, VA 23529

Virginia Tech
1424 S Main St.
Blacksburg, VA 24061

Principal Investigator(s)

Andrew Nichols (MU)

Chih-Sheng Chou (MU)

Mecit Cetin (ODU)

Montasir Abbas (VT)

Funding Source(s) and Amounts Provided (by each agency or organization)

USDOT: $180,000 (Federal)
Econolite: $100,000 (Match)
Marshall University: $30,000 (Match)
Old Dominion University: $25,000 (Match)
Virginia Tech: $25,000 (Match)

Total Project Costs

$180,000 (Federal) / $180,000 (Match)

Start Date


Completion Date



Actuated traffic signal controllers rely on sensors to detect vehicles so that green time can be allocated on a second-by-second basis. Traffic signals that are part of a closed loop system running coordination plans can also utilize detector information to select different pre-programmed plans based on the current traffic state. These Traffic Responsive Plan Selection (TRPS) algorithms currently rely on point detectors that only measure volume and occupancy. With the anticipated implementation of Connected Vehicles, sensors can be installed at signalized intersections to collect the trajectory of these vehicles, which will allow queue lengths to be estimated. Additionally, many radar-based sensors that are currently on the market are capable of tracking vehicles approaching an intersection, which can also be used to estimate queue lengths. This queue length information can be fused with the volume and occupancy data from point detectors to gain an even better understanding of the state of the signal system. This enhanced information could likely allow even better selection of pre-programmed coordination plans. When trajectory-based vehicle information becomes widespread and reliable, it is entirely possible that this information will be used by the controller logic to directly make decisions. In the meantime, this research will investigate whether this information can be leveraged to further enhance TRPS control, which is widely available in most traffic signal controllers. An existing Central system-in-the-loop simulation of a traffic signal system in Morgantown, WV will be utilized to implement and test algorithms for estimating queue lengths from vehicle trajectory data in real-time, estimating the state of the system in real-time, and communicating information back to the controllers to change the timing plans, when appropriate. The advanced TRPS will be compared to basic coordination timing plans and basic TRPS control across various volume scenarios to estimate improvements in delay, emissions, and fuel consumption.


Marshall University has been deploying Traffic Responsive Plan Selection (TRPS) on a signal system they manage for the WVDOT in Morgantown, WV. They have designed and tested TRPS parameters using a central system-in-the-loop environment that utilizes VISSIM, Econolite ASC/3 controllers, and Centracs central system management software. Therefore, the evaluation of the improved algorithm will be evaluated on “field” software. If the algorithm exhibits good performance based on existing radar detection trajectory data (since there is no CV data right now), it is possible that this could be deployed in the field at locations where radar detection is available. Econolite has indicated they might be interested in integrating the findings of this research into their software for other customers to use. It is also feasible that the results of this project could turn into a standalone application that runs as middleware for other controller manufacturers to process detector data and make TR decisions external to the controller. Even if the algorithm only performs well with CV data at certain market penetration rates, it could be deployed at that point in time.


Improved traffic control algorithms may result in more energy efficient transportation systems, through reduced stops and fuel consumption. Fewer stops and better traffic progression will also likely lead to reduced crash rates.