Moving vision zero programs forward: What pedestrian-focused countermeasure combinations work best and where? A dynamic copula-based time-series approach

Vision Zero stands out as one of the most promising systemic safety action plans. A crucial step to ensure the successful implementation of Vision Zero is to continuously assess the efficiency of the implemented treatments. Traditionally, this is achieved using before-and-after analyses or cross-sectional studies. However, the applicability of these approaches can be limited in assessing Vision Zero initiatives, which usually involve installing multiple treatments at a location, leading to a significant interdependency between treatments. This study proposes a dynamic R-vine copula-based time series model to evaluate the efficiency of treatments implemented as a part of Vision Zero. The proposed approach enables the accurate assessment of the treatments, understanding of their long-term impacts, and identifying the most effective combination of treatments at a location. The study also investigated the association between location characteristics and the performance of treatments. The proposed framework was applied to the City of Toronto at the macro-level (neighbourhood level) and focused on pedestrian-related treatments. Collision data and the implemented countermeasures were obtained from Toronto's Vision Zero Mapping Tool. The results show that the combination of speed limit reduction, leading pedestrian intervals (LPI), and community safety zones was the most frequent combination in terms of efficiency. Enforcement and speed limit reduction were the most effective combination in neighbourhoods with high school density, while LPI was effective in neighbourhoods with high densities of subway stations, and office density, especially when integrated with speed limit reduction and community zones. Driver feedback signs were effective in neighbourhoods with a high density of intersections, but only when combined with automated enforcement, community safety zones, and speed limit reduction. The results of the study would assist decision-makers in selecting the most effective treatment in a neighbourhood based on the neighbourhood characteristics and the countermeasures that are already installed.

An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours

The goal of this study is to provide an overview of previous research that investigated pedestrian violation behaviour, with a focus on identifying the contributing factors of such behaviour, its impact on pedestrian safety, the mitigation strategies, the limitations of current studies, and the future research directions. To that end, the Latent Dirichlet Allocation (LDA) text mining method was applied to extract a comprehensive list of studies that were conducted during the past 21 years related to pedestrian violation behaviours. Using the extracted studies, a multi-sectional literature review was developed to provide a comprehensive understanding of the different aspects related to pedestrian violations. Afterward, a meta-analysis was undertaken, using the studies that reported quantitative results, in order to obtain the average impact of the different contributing factors on the frequency of pedestrian violations. The study found that pedestrian violations are one of the hazardous behaviours that contribute to both the frequency and severity of pedestrian-vehicle collisions. According to the literature, the waiting time at the curbside, traffic volume, walking speed, pedestrian distraction, the presence of bus stops and schools, and the presence of on-street parking are among the key factors that increase the likelihood of pedestrian violations. The study has also reviewed a wide range of strategies that can be used to mitigate violations and reduce the safety consequences of such behaviour, including simple engineering-based countermeasures, enforcement, solutions that rely on advanced in-vehicle technologies, and infrastructure connectivity features, educational programs, and public campaigns.

An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions

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. To that end, the collision dataset of the City of Hamilton between 2010 and 2017 was reviewed to filter out pedestrian collisions that involved pedestrian violations. A Latent Class Analysis (LCA) method was applied to divide the dataset into a set of homogeneous clusters, based on traffic and intersection characteristics. 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. 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 reduction in collisions was observed with the increase in the frequency of buses at intersections that are located along main transit routes. Moreover, the presence of schools near the intersection tends to increase the frequency of collisions involving pedestrian violations, especially at large intersections. 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. The results of this study provide valuable insights for a better understanding of the safety consequences of pedestrian violations. Such understanding assists engineers and planners to design intersections that reduce the frequency of pedestrian violations and mitigate their negative safety consequences.

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study

OBJECTIVE

The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques.

METHODS

This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs.

RESULTS

The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.

CONCLUSIONS

The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.