Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
This paper addresses a key limitation of using existing detection and controller data of signalized intersections to build performance metrics in the automated traffic signal performance measures (ATSPMs) platform. Many intersections were installed with lane-by-lane stopbar detectors for actuated and adaptive signal controllers. Stopbar detector actuations are valuable inputs for signal controllers and intersection performance but are not the ideal advanced detector inputs for several key ATSPM metrics such as arrival on red (AoR) and Purdue coordination diagram (PCD). This paper presents a vehicle trajectory reconstruction algorithm based on shockwave theory to estimate advanced vehicle detections from stopbar arrivals and departures. Model parameters including shockwave speeds, free-flow velocity, acceleration, and deceleration rates were directly measured using spatial-temporal maps (STMaps) generated from roadside closed-circuit television (CCTV) camera video. The initially measured parameters were optimized using the genetic algorithm (GA) that was subsequently validated quantitively and qualitatively. Finally, combining the stopbar detector and signal phase and timing, a new coordination diagram was designed to enable traffic operators to identify mobility patterns and safety-critical events quickly. This research sought to utilize existing data sources to meet performance metric requirements while avoiding intensive investment to upgrade the current infrastructure. The STMap-based method substantially reduces the complexity of obtaining necessary model parameters. The new stopbar-based Rutgers coordination diagram enriches the visualization tools for intersection performance measures from controller and detection systems.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors confirm their contribution to the paper as follows: study conception and design: Tianya Zhang, Peter Jin, Thomas Brennan, Kelly McVeigh, Mohammad Jalayer; data collection: Kelly McVeigh, Thomas Brennan; analysis and interpretation of results: Tianya Zhang, Peter Jin, Thomas Brennan, Mohammad Jalayer, Deep Patel; draft manuscript preparation: Tianya Zhang, Peter Jin, Thomas Brennan, Mohammad Jalayer. All authors reviewed the results and approved the final version of the manuscript.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 29, 2021
Accepted: Jun 21, 2022
Published online: Jan 27, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 27, 2023
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Cited by
- Tianya Terry Zhang, Peter J. Jin, Benedetto Piccoli, Mina Sartipi, Deep spatial‐temporal embedding for vehicle trajectory validation and refinement, Computer-Aided Civil and Infrastructure Engineering, 10.1111/mice.13160, (2024).
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