Do stop-signs improve the safety for all road users? A before-after study of stop-controlled intersections using video-based trajectories and surrogate measures of safety

Bismarck Navarro; Luis Miranda-Moreno; Nicolas Saunier; Aurélie Labbe; Ting Fu

doi:10.1016/j.aap.2021.106563

Do stop-signs improve the safety for all road users? A before-after study of stop-controlled intersections using video-based trajectories and surrogate measures of safety

Accid Anal Prev. 2022 Mar:167:106563. doi: 10.1016/j.aap.2021.106563. Epub 2022 Feb 4.

Authors

Bismarck Navarro¹, Luis Miranda-Moreno², Nicolas Saunier³, Aurélie Labbe⁴, Ting Fu⁵

Affiliations

¹ Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 0C3, Canada. Electronic address: bismarck.ledezmanavarro@mail.mcgill.ca.
² Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec H3A 0C3, Canada. Electronic address: luis.miranda-moreno@mcgill.ca.
³ Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec H3C 3A7, Canada. Electronic address: nicolas.saunier@polymtl.ca.
⁴ Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada. Electronic address: aurelie.labbe@hec.ca.
⁵ College of Transportation Engineering, Tongji University, Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario N2L3G1, Canada. Electronic address: tingfu@tongji.edu.cn.

PMID: 35131654
DOI: 10.1016/j.aap.2021.106563

Abstract

Converting minor-approach-only stop (MAS) intersections to all-way-stop (AWS) intersections is a prevailing safety countermeasure in North American urban areas. Although the general population positively perceives the installation of stop-signs in residential areas, little research has investigated the impact of AWS on road safety and road user behaviour. This paper investigated the safety effectiveness of converting MAS to AWS intersections using an observational before and after approach and surrogate measures of safety. More specifically, the safety impacts of AWS conversion were investigated using multiple indicators, including vehicle speed measures, vehicle-pedestrian, vehicle-cyclist, vehicles-vehicle interactions as well as yielding rates before and after the treatment implementation. A multi-level regression approach was adopted to determine the effect of stop signs controlling for built environments, traffic exposure, and intersection geometry factors as well as site-specific unobserved heterogeneity. A unique sample of 31 intersections were used in this before-after study. From this sample, video data were collected before and after implementing AWS. In total, 245 h of video were automatically processed and corrected using a specialized computer vision software. More than 68,000 (37,668 before and 31,305 after AWS treatment) road user trajectories were obtained from 104 approaches. The results show that the conversion of MAS to AWS intersections significantly decreased vehicle speed and increased post-encroachment time. This work also shows that implementing AWS significantly increased the yielding rates from 45.7% to 76.7% in MAS conditions and reduced the average speed of motor-vehicles. Using multi-level regression model, it is estimated that when the intersection was converted from MAS to AWS, the minimum speed in the major approaches was reduced by 60.0%.

Keywords: All-way-stop; Cyclist speed; Distance-velocity framework; Minor-approach-stop; Post-encroachment time; Safety effectiveness; Surrogate measures of safety; Vehicle speed.

MeSH terms

Accidents, Traffic* / prevention & control
Controlled Before-After Studies
Environment Design
Humans
Motor Vehicles
Pedestrians*
Safety