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Estimating Switching Times of Actuated Coordinated Traffic Signals: A Deep Learning Approach
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2021-11-01
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Corporate Creators:Virginia Tech Transportation Institute ; Urban Mobility & Equity Center ; Morgan State University ; Virginia Polytechnic Institute and State University. Charles E. Via, Jr. Department of Civil and Environmental Engineering ; Virginia Polytechnic Institute and State University. Department of Computer Science
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Edition:Final Oct 2020- October 2021
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Abstract:Acceleration and deceleration maneuvers at signalized intersections are a major hindrance to fuel-efficient vehicle operations. Green Light Optimal Speed Advisory (GLOSA) allows for the control of vehicles in a fuel-efficient manner but requires reliable estimates of signal switching times. This study attempts to utilize data from actuated coordinated signalized intersections in Northern Virginia along with multiple deep learning and machine learning techniques to provide estimates of traffic signal switching times from green to red and vice versa. These estimates can be used to enable more fuel-efficient operation using GLOSA and eco-driving. They can also be used to mitigate dilemma zone safety concerns. A comparative analysis is conducted between the different techniques used and their pros and cons in terms of prediction errors and robustness to different traffic conditions.
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[PDF-2.56 MB]
