The vigorous progress in artificial intelligence and the widespread availability of computing
power and big data have resulted in remarkable achievements in applying deep learning …

As the global elderly population continues to rise, the risk of severe crashes among elderly
drivers has become a pressing concern. This study presents a comprehensive examination …

Daily activity pattern (DAP) prediction models within the Activity-based Modelling paradigm
are being currently developed without adequate consideration of the various …

This systematic literature review investigates the application of machine learning (ML)
techniques for predicting traffic incident durations, a crucial component of intelligent …

Early and accurate detection of anomalous events on the freeway, such as accidents, can
improve emergency response and clearance. However, existing delays and errors in event …

Quick and reliable automatic detection of traffic accidents is of paramount importance to
save human lives in transportation systems. However, automatically detecting when …

Early detection of accidents and rescue are of paramount importance in the reduction of
fatalities. Social media data, which has evolved to become an important source of sharing …

For many years, research has been primarily focused on enhancing our understanding of
the factors that impact the probability of vehicle crashes. The evaluation of safety has …

This article presents one of the pioneering studies on causal modeling in travel mode choice
decision-making using causal discovery algorithms. These models are a major …

This study compares the performance of a causal and a predictive model in modeling travel
mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal …