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Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures
| 题名： | Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures |
| 正文语种： | eng |
| 作者： | Tianya Terry Zhang;Peter J. Jin;Thomas M. Brennan Jr.;Kelly McVeigh;Mohammad Jalayer;Deep Patel |
| 作者单位： | Dept. of Civil and Environmental Engineering Rutgers State Univ. of New Jersey 500 Bartholomew Rd. Room 420A Piscataway NJ 08854-8099;Dept. of Civil and Environmental Engineering Rutgers State Univ. of New Jersey 500 Bartholomew Rd. Room 420F Piscataway NJ 08854-809;Dept. of Civil Engineering College of New Jersey Armstrong Hall RM 173 2000 Pennington Rd. Ewing Township NJ 08628;Supervising Highway Engineer Arterial Mobility Management Transportation Operations Systems and Support 1035 Parkway Ave. Trenton NJ 08625. Kelly;Dept. of Civil and Environmental Engineering Rowan Univ. 201 Mullica Hill Rd. Glassboro NJ 08028;Dept. of Civil and Environmental Engineering Rowan Univ. 201 Mullica Hill Rd. Glassboro NJ 08028 |
| 关键词： | Signal performance measures; Stopbar detector; Closed-circuit television (CCTV) camera. |
| 摘要： | 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.'. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 4 |
| 页码： | 04023014.1-04023014.17 |
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