Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we
use eXtreme Gradient Boosting (XGBoost)—a Machine Learning (ML) technique—to detect …

Large truck rollover crashes present significant financial, industrial, and social impacts. This
paper presents an effort to investigate the contributing factors to large truck rollover crashes …

Trucking plays a vital role in economic development in every country, especially countries
where it serves as the backbone of the economy. The fast growth of economy in Iran as a …

Automated vehicles are emerging on the transportation networks as manufacturers test their
automated driving system (ADS) capabilities in complex real-world environments in testing …

This study provides a comprehensive literature review to summarize all contributing factors
and the logit-based models that were used to predict the severity of crashes. Using the …

Almost all of the automatic accident detection (AAD) system suffers from the tradeoff
between computational overhead and detection accuracy. Recent advances in detection …

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 …

Motorcycles are becoming increasingly popular, especially in developing countries. This
increasing exposure, combined with the fact that they most likely result in injury crashes …

Microscopic traffic simulation is considered as a reliable tool in transportation planning and
management. Rational solutions from such simulations are contingent upon how well the …

Obtaining accurate speed and travel time information is a challenge for researchers,
geographers, and transportation agencies. In the past, traffic data were usually acquired and …