Using Big Data to Evaluate Traffic Signal Performance for the State of Tennessee

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Meleby, Piro A.J.D
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Middle Tennessee State University
Congestion on roadways has led to increased travel time, waste of fuel, and higher emissions. Compared with other methods such as constructing more lanes and/or roads, traffic signal retiming is a viable alternative way to mitigate congestion. To prioritize retiming and to understand the impact of signal retiming, the performance of signals needs to be evaluated. This thesis details a data-driven and low-cost methodology funded by the Tennessee Department of Transportation for evaluating the performance of traffic signals in Tennessee using third-party segmented probe vehicle data. Intersections were formed by matching the names and geographical information of inbound segments. The average speed, the worst-case travel time, and the bottleneck ranking data during different times of the day for each segment at an intersection were used to develop metrics for a ranking formula that outputs a numerical value from 0-10 for each intersection. The results were appended to an online database accessible via a webpage with search and sorting capabilities. The ranking formula results were compared with the Level-of-Service ranking provided by local traffic engineers in the Cities of Murfreesboro and Franklin. Additionally, testing was conducted on an industry-standard traffic signal controller for its data logging and networking functionalities. An Automated Traffic Signal Performance Measures (ATSPM) server and a website were also set up to display various performance measures of traffic signals. The traffic data was generated by emulating stop bar and advance loop detector events in a lab environment.