Efficient management of solid waste is essential to lessen its potential health and
environmental impacts. However, the current solid waste management practices encounter …

This study reviews the Artificial Intelligence and Machine Learning approaches developed
thus far for driver profile and driving pattern recognition, representing a set of macroscopic …

Prediction of mode choice for travelers has been the subject of keen interest among
transportation planners. Traditionally, mode choice analysis is conducted by statistical …

Electric vehicles (EVs) are the most important components of smart transportation systems.
Limited driving range, prolonged charging times, and inadequate charging infrastructure are …

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 …

Precise charging time prediction can effectively mitigate the inconvenience to drivers
induced by inevitable charging behavior throughout trips. Although the effectiveness of the …

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the
high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity …

Despite the measures put in place in different countries, road traffic fatalities are still
considered one of the leading causes of death worldwide. Thus, the reduction of traffic …

Examining the relationship between streetscape features and road traffic accidents is pivotal
for enhancing roadway safety. While previous studies have primarily focused on the …

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian
crash severity using data collected over five years (2016–2021) from Louisiana. The final …