Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …

With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

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 …

Accurate detection of traffic accidents as well as condition analysis are essential to
effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large
scale in China in the early 2017. Without docking stations, this system allows the sharing …

Big data analytics (BDA) has increasingly attracted a strong attention of analysts,
researchers and practitioners in railway transportation and engineering. This urges the …

Abstract Lane-changing (LC), which is one of the basic driving behavior, largely impacts on
traffic efficiency and safety. Modeling an LC process is challenging due to the complexity …

Millions of people use Twitter daily, posting thousands of messages and interacting with
their peers. This research aims to evaluate and classify the predictive potential of the Twitter …