Leading pedestrian intervals – Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis. (March 2023)

• Record Type: Journal Article
• Authors: Arun, Ashutosh; Lyon, Craig; Sayed, Tarek; Washington, Simon; Loewenherz, Franz; Akers, Darcy; Ananthanarayanan, Ganesh; Shu, Yuanchao; Bandy, Mark; Haque, Md. Mazharul
• Is Part Of: Accident analysis and prevention. Volume 181(2023)
• Publication Date: 2023-03
• Subjects: Leading pedestrian intervals -- Before-after evaluation -- Traffic conflict techniques -- Bayesian quantile regression -- Bayesian hierarchical extreme value theory -- Peak-over threshold approach; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106929
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A non-stationary framework for before-after analysis combining Bayesian Hierarchical Extreme Value analysis with Quantile Regression. Traffic conflicts were extracted by artificial intelligence-based advanced video analytics before and after the Leading Pedestrian Interval (LPI) treatment. Estimated conflict thresholds used with the non-stationary Bayesian Hierarchical Peak-Over Threshold model to estimate crash risk. Odds Ratio analysis suggests that LPI treatment reduced vehicle–pedestrian crash risks. The LPI treatment does not increase rear-end crash risk. Abstract: A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the "element of surprise" for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle–pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft's proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group's cloud-based SMART Safety TM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle–pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.