An origin–destination passenger flow prediction system based on convolutional neural network and passenger source-based attention mechanism

S Lv, K Wang, H Yang, P Wang - Expert Systems with Applications, 2024 - Elsevier
An accurate origin–destination (OD) passenger flow prediction system is crucially important
for urban metro operation and management. However, there are still lacking targeted …

Graph transformer embedded deep learning for short-term passenger flow prediction in urban rail transit systems: A multi-gate mixture-of-experts model

S Hu, J Chen, W Zhang, G Liu, X Chang - Information Sciences, 2024 - Elsevier
Urban rail transit (URT) plays a crucial role in mitigating urban traffic congestion by offering
faster and higher-quality travel services. Short-term passenger flow predictions have …

A new FCM-XGBoost system for predicting Pavement Condition Index

L Lin, S Li, K Wang, B Guo, H Yang, W Zhong… - Expert Systems with …, 2024 - Elsevier
Abstract Predicting Pavement Condition Index (PCI) is crucial for identifying potential
pavement distresses and develo** effective pavement maintenance strategies. Here, a …

A semi-supervised co-training model for predicting passenger flow change in expanding subways

K Wang, B Guo, H Yang, M Li, F Zhang… - Expert Systems with …, 2022 - Elsevier
Subways in many big cities are experiencing rapid development with new lines being
planned, built and put into operations. Understanding passenger flow change in expanding …

A new anomalous travel demand prediction method combining Markov model and complex network model

B Guo, M Li, M Zhou, F Zhang, P Wang - Physica A: Statistical Mechanics …, 2023 - Elsevier
Accurate prediction of travel demand is crucial for the development of intelligent
transportation systems. However, we are still lacking methods to predict travel demand in …

Exploring the association between network centralities and passenger flows in metro systems

A Kopsidas, A Douvaras, K Kepaptsoglou - Applied Network Science, 2023 - Springer
Network science offers valuable tools for planning and managing public transportation
systems, with measures such as network centralities proposed as complementary predictors …

An interpretable approach to passenger flow prediction and irregular passenger travel patterns understanding in metro system

F Wu, C Zheng, S Zhou, Y Lu, Z Wu, S Zheng - Expert Systems with …, 2025 - Elsevier
Metro passenger flow prediction is an essential aspect of intelligent transportation systems.
However, despite the emergence of deep learning technologies and the development of …

A graph deep learning model for station ridership prediction in expanding metro networks

F Ding, Y Liang, Y Wang, Y Tang, Y Zhou… - Proceedings of the 2nd …, 2024 - dl.acm.org
Due to their reliability, efficiency, and environmental friendliness, metro systems have
become a crucial solution to transportation challenges associated with urbanization. Many …

STContext: A Multifaceted Dataset for Develo** Context-aware Spatio-temporal Crowd Mobility Prediction Models

L Chen, J Fang, T Liu, F Gao, L Wang - arxiv preprint arxiv:2501.03583, 2025 - arxiv.org
In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models
leverage contextual features (eg, weather) to identify unusual crowd mobility patterns and …

[HTML][HTML] Exploring the Spatiotemporal Patterns of Passenger Flows in Expanding Urban Metros: A Case Study of Shenzhen

S Lv, H Yang, X Lu, F Zhang, P Wang - ISPRS International Journal of …, 2024 - mdpi.com
Despite extensive investigations on urban metro passenger flows, their evolving
spatiotemporal patterns with the extensions of urban metro networks have not been well …