In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Water quality monitoring is an important component of water resources management. In
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment.
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …

In this paper, we present a data augmentation technique to reproduce crash data. The
dataset comprising crash and non-crash events are extremely imbalanced. For instance, the …

Road accidents are one of the most relevant causes of injuries and death worldwide, and
therefore, they constitute a significant field of research on the use of advanced algorithms …

Highway work zones are most vulnerable roadway segments for congestion and traffic
collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions …

Purpose Deep learning (DL) technologies assist manufacturers to manage their business
operations. This research aims to present state-of-the-art insights on the trends and ways …

Real-time crash prediction is essential for proactive traffic safety management. However,
develo** an accurate prediction model is challenging as the traffic data of crash and non …

The real-time conflict prediction model using traffic flow characteristics is much less studied
than the crash-based model. This study aims at exploring the relationship between conflicts …