Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas.

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs).

How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands.

Introduction: The rates of road traffic injuries and fatalities in developing countries are significantly higher than in developed countries. This study examines the differences in driving behavior, road safety attitudes, and driving habits between a developed country (the Netherlands) and a developing country (Iran), which bear major differences in terms of crash involvement per population.

Method: In this context, this study assesses the statistical association of crash involvement with errors, lapses, aggressive driving incidents, and non-compliance with traffic rules, attitudes, and habits.

Capability Index: Applying a Fuzzy-Based Decision-Making Method to Evaluate Social Inclusion in Urban Areas Using the Capability Approach.

There is rising interest in re-evaluating transport policy, moving away from analysing concrete economic indicators and towards examining the number of persons participating in activities as 'social justice.' So, using the Capacity Approach, this study presents an aggregated model to calculate the level of capability in two neighbourhoods in Tehran, Iran, involving variables from the 'Individuals' and 'Environment' dimensions. The computed factors were then weighted by the Fuzzy AHP technique based on the opinion of Iranian and international experts and combined to produce the final Capability Index (CI).

Structural equation modelling of potential risk factors for pedestrian accidents in rural and urban roads.

Although several previous studies have focused on pedestrian safety, most of the studies have focused on urban roads. This study investigated accident data among 1358 pedestrians from 2012 to 2018 on urban, suburban, and rural roads in 16 cities in the Gilan province of Iran using structural equation modelling (SEM). Factor analysis showed that four exogenous latent variables had a significant relationship with fatal accidents among pedestrians.

Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV).

A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians. Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models. Despite numerous studies on pedestrian safety based on various roads, outskirt areas have not been considered.

Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran.

Introduction: Digital billboards (DBs) are a competing factor for attracting drivers' attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers' attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers' behavior and measure factors responsible for drivers' distraction in Babol, Iran, as a case study.