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 …

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 …

Distracted and impaired driving is a key contributing factor in crashes, leading to about 35%
of all transportation-related deaths in recent years. Along these lines, cognitive issues like …

This paper aims to contribute to the literature by conducting a causation study for large-truck
involved crashes. Detailed records of crashes involving large trucks occurred in the state of …

The emergence of new sensors and data sources provides large scale high-resolution big
data from instantaneous vehicular movements, driver decision and states, surrounding …

Crowdsourcing refers to the acquisition of data from users who contribute their information
via smartphone, social media, or the internet. In transportation systems, crowdsourcing turns …