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First published online January 1, 2016

Efficient Priority Control Model for Multimodal Traffic Signals

Abstract

The paper presents a model for multimodal traffic signal priority control. The approach is based on an analytical model and a flexible implementation algorithm that considers real-time vehicle actuation. The analytical model considers the needs of different modes in a real-time connected vehicle environment. The model provides an optimal signal schedule that minimizes the total weighted priority request delay. The flexible implementation algorithm is designed to guarantee that the optimal signal schedule is applied with minimum negative impact on regular vehicles. The model has been tested in a simulation framework on two networks: San Mateo, California, and Anthem, Arizona. The simulation experiments showed that the model, when compared with fully actuated control, was able to reduce average delay and travel times for priority vehicles without a significant negative impact on passenger cars. The field results of implementing the priority framework in the nationally affiliated connected vehicles test bed in Anthem showed the effectiveness of the model in the real world.

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References

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Article first published online: January 1, 2016
Issue published: January 2016

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© 2016 National Academy of Sciences.
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Authors

Affiliations

Mehdi Zamanipour
Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, and Turner–Fairbank Highway Research Center, FHWA, 6300 George Washington Memorial Parkway, McLean, VA 22102
K. Larry Head
Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, 1127 East James E. Rogers Way, P.O. Box 210020, Tucson, AZ 85721-0012
Yiheng Feng
University of Michigan Transportation Research Institute, 2901 Baxter Road, Ann Arbor, MI 48109
Shayan Khoshmagham
Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, 1127 East James E. Rogers Way, P.O. Box 210020, Tucson, AZ 85721-0012

Notes

M. Zamanipour, [email protected].

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