Injury severity prediction of traffic crashes with ensemble machine learning techniques: A comparative study

A Jamal, M Zahid, M Tauhidur Rahman… - … journal of injury …, 2021 - Taylor & Francis
A better understanding of injury severity risk factors is fundamental to improving crash
prediction and effective implementation of appropriate mitigation strategies. Traditional …

Transparent deep machine learning framework for predicting traffic crash severity

K Sattar, F Chikh Oughali, K Assi, N Ratrout… - Neural Computing and …, 2023 - Springer
Abstract Analysis of crash injury severity is a promising research target in highway safety
studies. A better understanding of crash severity risk factors is vital for the proactive …

Automated vehicle data pipeline for accident reconstruction: New insights from LiDAR, camera, and radar data

J Beck, R Arvin, S Lee, A Khattak… - Accident Analysis & …, 2023 - Elsevier
As automated vehicles are deployed across the world, it has become critically important to
understand how these vehicles interact with each other, as well as with other conventional …

Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways

H Wen, Z Ma, Z Chen, C Luo - Accident Analysis & Prevention, 2023 - Elsevier
Many studies examine the road characteristics that impact the severity of truck crash
accidents. However, some only analyze the effect of curves or slopes separately, ignoring …

The dilemma of road safety in the eastern province of Saudi Arabia: Consequences and prevention strategies

A Jamal, MT Rahman, HM Al-Ahmadi… - International journal of …, 2020 - mdpi.com
Road traffic crashes (RTCs) are one of the most critical public health problems worldwide.
The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per …

The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

R Arvin, M Kamrani, AJ Khattak - Accident Analysis & Prevention, 2019 - Elsevier
While the cost of crashes exceeds $1 Trillion a year in the US alone, the availability of high-
resolution naturalistic driving data provides an opportunity for researchers to conduct an in …

A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence

H Yu, Z Li, G Zhang, P Liu - Analytic methods in accident research, 2019 - Elsevier
Temporal variation has been recognized as one of the major sources of unobserved
heterogeneity in traffic safety research that has not been completely addressed. Overlooking …

Impact of traffic and road infrastructural design variables on road user safety–a systematic literature review

A Choudhary, RD Garg, SS Jain… - International Journal of …, 2024 - Taylor & Francis
Road transportation is more favoured as it is less expensive and relatively faster than other
modes of transportation. A higher probability of getting involved in crashes reveals its …

Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes

T Champahom, S Jomnonkwao… - Accident Analysis & …, 2020 - Elsevier
A rear-end crash is a widely studied type of road accident. The road area at the crash scene
is a factor that significantly affects the crash severity from rear-end collisions. These road …

Vehicle collisions analysis on highways based on multi-user driving simulator and multinomial logistic regression model on US highways in Michigan

A IM Almadi, RE Al Mamlook, I Ullah… - International journal …, 2023 - Taylor & Francis
Traffic collision on the highway has become a serious issue because they delay the
sustainable development of society. Highway accidents on I-69 have one of the highest …