A real-time adaptive signal control in a connected vehicle environment |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of Virginia, P.O. Box 400742, Charlottesville, VA 22904-4742, United States;2. Department of Transportation and Urban Infrastructure Studies, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251, United States;1. Department of Civil, Structural, and Environmental Engineering and Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, United States;2. Environmental and Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, United States;3. Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721, United States;1. School of Transportation Science and Engineering, Harbin Institute of Technology, China;2. Office of Operation Research and Development, Federal Highway Administration, United States;3. Department of Transport & Planning and Department of BioMechanical Engineering, Delft University of Technology, Netherlands;4. Department of Civil and Environmental Engineering, University of Virginia, United States;1. University of Florida, 365 Weil Hall, PO Box 116580, Gainesville, FL 32611, United States;2. Department of CISE, University of Florida, Gainesville, FL 32611, United States;1. Civil and Environmental Engineering Department, Washington State University, USA;2. Civil and Environmental Engineering Department, Washington State University, PO Box 642910, Pullman, WA 99164-2910, USA |
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| Abstract: | The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way. |
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| Keywords: | Adaptive traffic signal control Connected vehicle Dynamic programming Estimating vehicle states Real-time optimization |
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