Such understanding assists engineers and planners to design intersections that reduce the frequency of pedestrian violations and mitigate their negative safety consequences. The results of this study provide valuable insights for a better understanding of the safety consequences of pedestrian violations. The results also revealed that the presence of central refuge islands, despite their overall safety benefits, increases the likelihood of collisions involving pedestrian violations in large intersections. Moreover, the presence of schools near the intersection tends to increase the frequency of collisions involving pedestrian violations, especially at large intersections. A reduction in collisions was observed with the increase in the frequency of buses at intersections that are located along main transit routes.

The results showed that the number of bus stops within the intersection area is directly associated with the frequency and the severity of collisions involving pedestrian violations. A copula-based multivariate model was then developed for each cluster in order to study the impact of the different factors on collisions under the prevailing conditions of each cluster. A Latent Class Analysis (LCA) method was applied to divide the dataset into a set of homogeneous clusters, based on traffic and intersection characteristics. To that end, the collision dataset of the City of Hamilton between 20 was reviewed to filter out pedestrian collisions that involved pedestrian violations. The main goal of this study is to investigate the impact of a variety of factors on the frequency and the severity of pedestrian-vehicle collisions that involve pedestrian violations.

Previous studies adopted various methods to study the impact of those factors on pedestrian violations, including traditional statistical models (Pawar and Patil, 2016 Chen et al., 2017 Ni et al., 2017), cross-sectional and time-series analysis (Fu and Zou, 2016 Guo et al., 2016), surveys (Chu et al., 2004 Ren et al., 2011), Machine learning techniques (Papadimitriou et al., 2013 Zhang et al., 2020), and micro-simulation analysis (Zaki et al., 2013 Hediyeh et al., 2014).

According to the literature, pedestrian violations have been tied with traffic-related factors (Zhu et al., 2021 Yoneda et al., 2019 Naser et al., 2017), intersection-specific characteristics (Ishaque and Noland, 2008 Cao et al., 2016), built environment factors (Miranda-Moreno et al., 2011 Oakes et al., 2007), pedestrian attitudes (Zareharofteh et al., 2021 Aghabayk et al., 2021 Russo et al., 2018), and environmental features (Zhu et al., 2021 Wang et al., 2011 Zhang et al., 2016). The main focus of those studies was to identify the key contributing factors that impact pedestrian violations.

Perception 6 by The Island