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When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS...

Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending i...

Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the ...

With the known evidence that potential connected vehicle (CV) users are worried about sharing data because of the associated data privacy and security issues, this study investigates the importance of the reputation of t...

Trajectory reconstruction from sparse and noisy GPS data is critical for applications such as urban mobility analysis, transportation planning, and navigation systems. However, large sampling intervals and the typically ...

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approac...

An appropriate understanding and accurate estimation of zonal freight production (FP) and attraction (FA) volume is one of the most important topics and the initial step in developing the origin and destination (OD) matr...

Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of commun...

Traditional travel surveys are costly, time-consuming and face declining response rates, motivating the exploration of artificial data generation methods. In this research, we propose a novel Persona-Driven Method (PDM) ...

Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific...

Cross-city transfer learning (CCTL) has emerged as a crucial approach for managing the growing complexity of urban data and addressing the challenges posed by rapid urbanization. This paper provides a comprehensive revie...

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth, safe, and green transportation. Therefore, effective traffic monitoring is an essential topic a...

While physics models for predicting system states can reveal fundamental insights owing to their parsimonious structure, they may not always yield the most accurate predictions, particularly for complex systems. As an al...