Investigating the contributing factors to autonomous Vehicle-Road user Conflicts: A Data-Driven approach.

With the imminent widespread integration of Autonomous Vehicles (AVs) into our traffic ecosystem, understanding the factors that impact their safety is a vital research area. To that end, this study assessed the impact of a wide range of factors on the frequency of AV-road user conflicts. The study utilized the Woven prediction and validation dataset, which contains over 1000 h of data collected from the onboard sensors of 20 AVs in California.

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.

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.

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.

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.