United States Department of Transportation
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Fathoming the Maximum Potential for Freight Sensitive Intersection Control
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2020-12-01
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[PDF-896.98 KB]
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Edition:Final Report
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Abstract:This project primarily studies signal timing with special consideration of freight traffic in urban areas. The rationale is that freight logistics are critical to the quality of life and economies. However, freight mobility, especially along major freight corridors in urban areas, rarely gets special consideration in signal timing. The advent of the Internet of Things (IoT) makes vast information collection a reality. The rich data environment, combined with the boost in computational power, has brought unprecedented opportunities closer to reality than ever for real-time, information-driven intersection traffic control under variants of traffic scenarios. The research advances conventional traffic signal control through delay dynamics to design highly efficient network control algorithms. This research focuses on developing a new traffic-responsive network signal control in general, and especially with freight traffic considered. The optimal conditions for the waiting time dynamics are studied, and a new flow-based signal control algorithm is proposed. The derivation process is simple and follows the standard optimization problem-solving path. The algorithm is implemented by Python and tested in SUMO. The numerical tests are conducted on two types of networks, a single corridor and a local grid network, under three traffic demand scenarios: low, medium, and heavy. The effect of different truck ratios (0%, 10%, 25%, and 40%) on each control algorithm was tested simultaneously for the same major and minor traffic volume scenarios. Compared with existing signal control algorithms, the result shows that the MaxFlow performs better than fixed-time and DORAS-Q in terms of the average vehicle waiting time under varying volumes, in both arterial and grid network cases, with various truck flow ratios.
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