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Civil engineers honored for congestion mitigation project

Traffic congestion studyLamar University’s Xing Wu, assistant professor in the Department of Civil and Environmental Engineering, along with Hao Yang, adjunct assistant professor, and a group of graduate students, recently received honors from the Texas Department of Transportation (TxDOT) and the American Association of State Highway and Transportation Officials (AASHTO) for their research project “0-6920 Proactive Traffic Signal Timing and Coordination for Congestion Mitigation,” which received $60,000 from TxDOT when initially proposed.

AASHTO Research Advisory Committee (RAC) Value of Research Task Force solicits states to select projects that are considered “High Value Research projects” for examples of “Transportation Excellence Through Research.” Projects are split based on region and go through several rounds of voting.

Wu, who joined LU’s faculty in 2012, holds a Ph.D. in transportation engineering from Northwestern University, Evanston, Illinois. Yang holds a Ph.D. in civil engineering from the University of California, Irvine. The project received enough votes in the first round to be placed in the “final four,” the only project to receive the honor. The project will be represented at the Transportation Research Board (TRB) Annual Meeting in July.

Traffic congestion studyWu’s project focuses on traffic streams on arterial roads that witness increased congestion throughout peak and non-peak periods. “Vehicles are forced to stop at signals, increasing travel time, emission and fuel consumption,” said Wu, “so it was our goal to create a process that relies on signal phasing, timing planning and signal coordination systems.”

First, the proposed system modeled vehicle queue generation and propagation ahead of traffic signals and found the relationship between road congestions and delays. The congestion was then reproduced with traffic simulation software. Second, a communication platform was constructed for connected vehicles, onsite loop detectors and traffic signals to monitor real-time traffic conditions. Finally, based on the results, a proactive signal strategy was developed. To ensure the effectiveness of the strategy, a model control was introduced to predict short-term traffic conditions and provide more intelligent signal phasing under various traffic statuses.