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 …

Vehicle sharing, automation, and electrification are anticipated to move mobility and society
towards low carbon and sustainability. Yet their positives and negatives to the society and …

Autonomous mobility is one of the rapidly evolving aspects of smart transportation which
carries the potential of resha** both demand and supply sides of transportation systems …

It is well known that faster and less-expensive transit systems with shorter walking distances,
as well as less transfers, are associated with transit use. In this regard, recent studies have …

The field of travel demand analysis has traditionally been dominated by statistical models.
Conversely, Machine Learning (ML) techniques have been rapidly emerging in the past few …

Abstract Automated Vehicles (AVs) are poised to disrupt travel patterns and the
sustainability of transportation networks. Conventional methods for studying these changes …

The first and last mile (FLM) of transit trips are associated with physical activity, often placing
a burden on transit users. FLMs that are short, safe, and convenient to walk could facilitate …

Introduction and objective COVID-19 has transformed economic activities and travel
behavior, especially for public transport use. When a pandemic ebbs, clarifying travel …

Mass transit is a key component of a sustainable transportation system in urban networks. In
this research, we propose a continuum approximation model to optimize the line spacing …

In an effort to understand public acceptance and adoption behavior of smart vehicles
technologies, this paper presents a detailed analysis of consumer preference for electric and …