Machine learning (ML) prediction determinants based on open data (OD) are investigated in
this work, which is accomplished by examining current research trends over ten years …

Purpose The purpose of this study is to present a large-scale real-world comparative study
using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment …

With advanced artificial intelligence and deep learning techniques, a growing number of
data sources are playing more and more critical roles in planning and operating …

Different from the existing train delay studies that had strived to explore sophisticated
algorithms, this paper focuses on finding the bound of improvements on predicting multi …

Predictive analytics is an increasingly popular tool for enhancing decision-making
processes but is in many business settings based on rule-based models. These rule-based …

Abstract The coronavirus disease 2019 (COVID-19) pandemic that started at the beginning
of the year 2020 has significantly disrupted people's daily life around the world …

In many big cities, train delays are among the most complained-about events by the public.
Although various models have been proposed for train delay prediction, prior studies on …

Reliability and punctuality are the key evaluation criteria in railway service for both
passengers and operators. Delays spanning over spatial and temporal dimensions …

This chapter presents a methodology of bus travel time prediction, which is driven by the
state-of-the-art machine learning technologies and involves real-time bus GPS location data …

In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS)
have witnessed significant growth thus improving the smart transportation concept. The …