ENHANCING PEDESTRIAN SAFETY BY PROVIDING A LIDAR-BASED ANALYSIS OF JAYWALKING CONFLICTS AT SIGNALIZED INTERSECTIONS
ENHANCING PEDESTRIAN SAFETY BY PROVIDING A LIDAR-BASED ANALYSIS OF JAYWALKING CONFLICTS AT SIGNALIZED INTERSECTIONS

Summary/Abstract: In response to the inherent vulnerability of pedestrians in urban settings, this paper is driven by a commitment to enhancing their mobility and safety. Recognizing the prevalence of jaywalking as a significant concern, the study seeks practical solutions through the application of LiDAR sensors at signalized intersections. By delving into the complexities of jaywalking events and their contributing factors, the research aims to provide valuable insights that extend beyond mere statistical analysis. The motivations behind this endeavour lie in the imperative to comprehensively understand and address the risks associated with jaywalking, ultimately fostering a safer environment for pedestrians navigating urban crossroads.Aim: The primary aim of this paper is to assess and analyse the diverse factors inf luencing the frequency of jaywalking at signalized intersections, leveraging the capabilities of LiDAR sensors for safety applications. Through a meticulous examination of 1000 jaywalking events detected over a six-month period, the study aims to pinpoint highly correlated independent variables to the frequency of jaywalking events. These variables include traffic signal controller patterns, signal phases, vehicle-pedestrian conf licts, weather conditions, vehicle volume, walking patterns toward the median, pedestrian volume, and the unique jaywalker’s ratio. Employing advanced statistical regression models, the research seeks to identify optimal models and unravel key insights into the nuanced dynamics of jaywalking behaviour. The overarching goal is to equip decision-makers and transportation specialists with data-driven knowledge, enabling them to implement targeted safety measures that mitigate pedestrian risks and enhance safety infrastructure at critical urban crossroads.Results: The outcomes of the study, derived from the optimal Poisson regression model, yield crucial insights into the multifaceted nature of jaywalking events at signalized intersections. The morning and mid-day signal controller patterns exhibit a substantial decrease of 44.7% and 34.4%, respectively, compared to the evening (PM) pattern, shedding light on temporal nuances in jaywalking behaviour. Additionally, the severity of vehicle-pedestrian conf licts escalates proportionally with the number of jaywalkers, emphasizing the importance of addressing pedestrian f low in mitigating potential conf licts. Notably, the presence of vegetation in the median emerges as a significant factor, significantly morgan.eduincreasing the frequency of jaywalking. These results contribute to a nuanced understanding of the intricate interplay between environmental, temporal, and behavioural factors in jaywalking incidents. Decision-makers and transportation specialists can leverage these findings to formulate targeted safety interventions, fostering a safer pedestrian experience at crucial urban crossroads.

• Details
• Contents