Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis |
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| Institution: | 1. Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada;2. Department of Civil Engineering and Applied Mechanics, McGill University, Room 391, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 0C3, Canada;3. Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec H3C 3A7, Canada;4. Department of Civil Engineering and Applied Mechanics, McGill University, Room 268, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 0C3, Canada;1. Laboratório Nacional de Engenharia Civil, Departamento de Transportes, Núcleo de Planeamento, Tráfego e Segurança Av. do Brasil 101, Lisboa 1700-066, Portugal;2. Department of Civil and Environmental Engineering, Rowan University, Glassboro, NJ 08028, United States;1. Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada;2. Intactlab – Data Science, Intact Insurance, Suite 100, 2020 Boulevard Robert-Bourassa, Montréal, Québec H3T 2A7, Canada;3. Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada;4. Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec H3C 3A7, Canada;5. Department of Civil Engineering and Applied Mechanics, McGill University, Room 268, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 0C3, Canada;6. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) Pavillon André Aisenstadt, Room 3520 2920 Chemin de la Tour Université de Montréal, Montréal, Quebec H3T 1J4, Canada;1. Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 2K6, Canada;2. Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succursale Centre-Ville, Montréal, Québec H3C 3A7, Canada;1. School of Transportation Science and Engineering, Harbin Institute of Technology, China;2. School of Transportation, Southeast University, China;3. Department of Technology and Society, Faculty of Engineering, LTH Lund University, Lund, Sweden;1. Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada;2. Civil Engineering and Built Environment School, Queensland University of Technology (QUT), 2 George Street, GPO Box 2434, Brisbane, QLD 4001, Australia;3. School of Civil Engineering, University of Queensland, St. Lucia 4072, Brisbane, Australia;1. Department of Civil and Urban Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA;2. Department of Civil & Environmental Engineering, Old Dominion University (ODU), 129C Kaufman Hall, Norfolk, VA 23529, USA;3. Department of Computational Modeling and Simulation Engineering, Old Dominion University, 4700 Elkhorn Ave, Norfolk, VA 23529, USA |
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| Abstract: | Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control. |
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| Keywords: | Traffic control Stop signs Computer vision Pedestrians Surrogate safety Regression models |
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