Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Gavric, Slavica; Dobrota, Nemanja; Erdagi, Ismet Goksad; Stevanovic, Aleksandar; Osman, Osama A.

doi:10.1177/03611981221150394

Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Journal Article · Tue Feb 07 23:00:00 EST 2023 · Transportation Research Record: Journal of the Transportation Research Board

DOI:https://doi.org/10.1177/03611981221150394· OSTI ID:2418082

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Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA; OSTI
Kittelson and Associates, Inc., Washington, DC
Civil and Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
Mobility Thrust Lead, Center for Urban Informatics and Progress, University of Tennessee, Chattanooga, TN

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.

Research Organization:: Univ. of Tennessee, Knoxville, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: EE0009208

OSTI ID:: 2418082

Journal Information:: Transportation Research Record: Journal of the Transportation Research Board, Journal Name: Transportation Research Record: Journal of the Transportation Research Board Journal Issue: 6 Vol. 2677; ISSN 0361-1981

Publisher:: SAGE

Country of Publication:: United States

Language:: English

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