Origin-destination (OD) flow modeling is an extensively researched subject across multiple
disciplines, such as the investigation of travel demand in transportation and spatial …

Accurate short-term forecasting of public transport demand is essential for the operation of
on-demand public transport. Knowing where and when future demands for travel are …

An accurate origin–destination (OD) passenger flow prediction system is crucially important
for urban metro operation and management. However, there are still lacking targeted …

In this article, we propose a new method for OD (Origin–Destination)​ matrix prediction
based on traffic data using deep learning. The input values of the developed model were …

Metro lines of some mega cities usually suffer from extreme congestions in peak hours,
leading to serious operation risks. To relieve the extreme saturation for overcrowded metro …

Time series analysis has been used extensively in transport research in various areas, such
as traffic management and transport planning. Time-series data may contain temporal and …

Rational use of urban underground space (UUS) and public transportation transfer
underground can solve urban traffic problems. Accurate short-term prediction of passenger …

As the scale of urban rail transit (URT) networks expands, the study of URT resilience is
essential for safe and efficient operations. This paper presents a comprehensive review of …

Accurate prediction of short-term origin-destination (OD) matrix is crucial for operations in
metro systems. Recently, some deep learning-based models have been proposed for OD …

Information about spatial distribution (OD flows) is a key element in traffic management
systems in urban transport networks that enables efficient traffic control and decisions to …