Behavioral and psychological determinants of pedestrian collisions on arterial roads with evidence from random parameter models.

Arterial roads, while comprising a small percentage of total roadway mileage in the U.S., contribute disproportionately to pedestrian fatalities.

Crash outcomes of yellow school buses: Random parameter and correlated random parameter logit models with heterogeneity in means.

Despite rigorous safety standards, school buses in the United States still experience around 26,000 crashes annually, resulting in approximately 10 fatalities, with a fatality rate persistently static despite advances in vehicle safety. Such crashes often have significant implications, particularly for the young passengers involved. This study utilized a novel approach by analyzing Texas Crash Records Information System (CRIS) data from 2017 to 2022 through a Random Parameter Logit Model with Heterogeneity in Means (RPLHM) and Correlated RPLHM (CRPLHM).

Pattern recognition in crash clusters involving vehicles with advanced driving technologies.

Autonomous Vehicle (AV) technologies, including Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), have significant potential to reduce crashes caused by driver errors. However, as AVs become more prevalent on roadways, the number of crashes involving them is also increasing. While considerable research has explored factors contributing to AV crashes, a gap remains in understanding the critical risk factor patterns within clusters of ADAS- and ADS-engaged AV crashes.

Uncovering the role of restraint usage in driver ejection: a data mining investigation of fatal and injury crashes.

Objective: This study aims to identify key contributing factors to driver ejection in vehicle crashes. By analyzing crash characteristics, restraint usage, and environmental conditions, the study provides data-driven insights to mitigate ejection risks and improve roadway safety interventions.

Method: This study, utilizing comprehensive Texas crash data from 2017 to 2022; investigates this critical concern by applying an ARM algorithm to uncover key patterns and interactions among various factors contributing to ejection events.

A hybrid data mining framework to investigate roadway departure crashes on rural two-lane Highways: Applying Fast and Frugal Tree with Association Rules Mining.

The complexity of factors contributing to roadway departure (RwD) crashes on rural highways necessitate advanced analytical approaches to enhance traffic safety. This study presents a hybrid data mining framework that combines the Fast and Frugal Tree (FFT) and Association Rules Mining (ARM) algorithms to identify the patterns of RwD crashes on rural 2-lane highways in Louisiana state. The research is focused on addressing two critical research questions (RQ), RQ1: Which variable features contribute to the fatal-severe RwD crashes? RQ2: Focusing on the identified top factors contributing to fatal-severe RwD crashes, how co-occurrence of different crash-contributing factors increase the likelihood of RwD crashes? For the analysis, this research team collected crash data from the Louisiana Department of Transportation and Development, encompassing a total of 22,406 unique RwD crashes on rural 2-lane highways.

Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet).

Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment vehicles to examine the factors influencing crash severity, with a particular focus on comparing incidents on county roads to those on non-county roads.

Assessing motorcyclist injury severity on curved road segments with temporal dynamics and unobserved heterogeneity.

Motorcyclists face an increased risk of injury severity at curved roadway segments due to a wide range of factors. Given the complex and evolving nature of factors influencing motorcyclist injury severity on curved roadway segments, there is a need for research that not only captures these contributing factors dynamics but also considers temporal variations and unobserved heterogeneity. The study used mixed logit models to analyze 8815 motorcycle crashes that occurred on curved segments in Texas between 2017 and 2022.

Children on wheels: Identifying crash determinants using cluster correspondence analysis.

Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes and uncover distinct crash patterns using a dataset of 2,394 crashes in Texas from 2017 to 2022. Employing a hybrid approach through machine learning models XGBoost and Random Forest, and Cluster Correspondence Analysis (CCA), the research identified six clusters characterized by unique crash factors and patterns.

The negative Binomial-Lindley model with Time-Dependent Parameters: Accounting for temporal variations and excess zero observations in crash data.

Crash counts are non-negative integer events often analyzed using crash frequency models such as the negative binomial (NB) distribution. However, due to their random and infrequent nature, crash data usually exhibit unique characteristics, such as excess zero observations that the NB distribution cannot adequately model. The negative binomial-Lindley (NBL) and random parameters negative binomial-Lindley (RPNBL) models have been proposed to address this limitation.

Analyzing fatal crash patterns of recidivist drivers across genders and age Groups: A hazard-based duration approach.

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a previous non-fatal crash and a subsequent fatal crash, taking into account the age and gender of the driver. By identifying the characteristics of this unique driver population and the factors that influence the duration between their crash events strategies can be put in place to prevent the occurrence of future and potentially fatal crashes.

Advancing traffic safety through the safe system approach: A systematic review.

The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road users, safe vehicles, safe speeds, safe roads, and post-crash care. The review provides insights into SSA's effectiveness in reducing road traffic fatalities and injuries, exploring implementation challenges and opportunities, including policy initiatives, institutional frameworks, and stakeholder collaborations.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means.

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway.

Modeling the lagged impacts of hourly weather and speed variation factors on the segment crash risk of rural interstate freeways: Applying a space-time-stratified case-crossover design.

In the realm of traditional roadway crash studies, cross-sectional modeling methods have been commonly employed to investigate the intricate relationship between the crash risk of roadway segments and variables including roadway geometrics, weather conditions, and speed distribution. However, these methodologies assume that the explanatory variables and target variable are only associated within the same time period. Although this assumption is well-founded for static factors like roadway geometrics, it proves inadequate when dealing with highly time-varying variables related to weather conditions and speed variation.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis.

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level.

Crashes involving distracted pedestrians: Identifying risk factors and their relationships to pedestrian severity levels and distraction modes.

The concept of distracted pedestrians and its impact on highway safety has gained increasing attention in recent years. However, studies focusing exclusively on distracted pedestrian crashes are less pervasive than distracted driving. In addition, most prior studies investigate the harmful effect of cellphone usage while walking, without considering other forms of pedestrian distraction.

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions.

Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored.

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).

Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes.

Emergency vehicle crashes, involving police vehicles, ambulances, and fire trucks, pose a serious traffic safety concern causing severe injury and deaths to first responders and other road users. However, limited research is available focusing on the contributing factors and their interactions related to these crashes. This research aims to address this gap by 1) identifying patterns of emergency vehicle crashes based on severity levels in both emergency and non-emergency modes and 2) comparing the associations by response modes for the related fatal, nonfatal injury, and no-injury crashes.

Factors influencing safety perceptions of sharing roadways with autonomous vehicles among vulnerable roadway users.

Introduction: The operation of autonomous vehicles (AVs) on public roadways affects the safety of vulnerable roadway users, such as pedestrians and bicyclists. This research contributes to the literature by investigating vulnerable roadway users' safety perceptions on road sharing with AVs.

Method: This study analyzed the survey responses of pedestrians and bicyclists in Pittsburgh, Pennsylvania, collected by Bike Pittsburgh (Bike PGH) in 2017 and 2019.

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.

Understanding the drowsy driving crash patterns from correspondence regression analysis.

Unlabelled: Drowsy driving-related crashes have been a key concern in transportation safety. In Louisiana, 14% (1,758 out of 12,512) of police-reported drowsy driving-related crashes during 2015-2019 resulted in injury (fatal, severe, or moderate). Amid the calls for action against drowsy driving by national agencies, it is of paramount importance to explore the key reportable attributes of drowsy driving behaviors and their potential association with crash severity.

Using unsupervised learning to investigate injury-associated factors of animal-vehicle crashes.

Animal vehicle crash is a critical yet often under-emphasized safety concern of Louisiana. During 2014-2018, over 14,000 animal-related crashes cost Louisiana more than $520 million. To identify multiple key contributing factors and their association patterns, this study applied association rules mining in the dataset of animal-related roadway crashes that occurred during 2014-2018.