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Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
https://orcid.org/0000-0002-7606-9886
https://orcid.org/0000-0002-7688-3730
https://orcid.org/0000-0003-2316-4983
https://orcid.org/0000-0001-7595-7489
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.
Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
https://orcid.org/0000-0002-7606-9886
https://orcid.org/0000-0002-7688-3730
https://orcid.org/0000-0003-2316-4983
https://orcid.org/0000-0001-7595-7489
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.
Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
https://orcid.org/0000-0002-7606-9886
https://orcid.org/0000-0002-7688-3730
https://orcid.org/0000-0003-2316-4983
https://orcid.org/0000-0001-7595-7489
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.
Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
J. Transp. Eng., Part A: Systems
Zhang, Tianya Terry (author) / Jin, Peter J. (author) / Brennan, Thomas M. (author) / McVeigh, Kelly (author) / Jalayer, Mohammad (author) / Patel, Deep (author)
2023-04-01
Article (Journal)
Electronic Resource
English
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