Traffic prediction, a critical component for intelligent transportation systems, endeavors to foresee future traffic at specific locations using historical data. Although existing traffic …
Y Liu, S Rasouli, M Wong, T Feng, T Huang - Information Fusion, 2024 - Elsevier
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Multivariate Time Series (MTS) widely exists in real-word complex systems, such as traffic and energy systems, making their forecasting crucial for understanding and influencing …
Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often …
J Wang, C Yang, X Jiang, J Wu - Proceedings of the 29th ACM SIGKDD …, 2023 - dl.acm.org
Given its broad applications, time series analysis has gained substantial research attention but remains a very challenging task. Recent years have witnessed the great success of deep …
L Li, H Wang, W Zhang, A Coster - arXiv preprint arXiv:2403.12418, 2024 - arxiv.org
Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non- stationary, leading to the continuous challenge of spatial-temporal graph learning. In the …
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in …
B Wang, P Wang, Y Zhang, X Wang, Z Zhou… - Proceedings of the …, 2024 - ojs.aaai.org
With the progress of urban transportation systems, a significant amount of high-quality traffic data is continuously collected through streaming manners, which has propelled the …
The study of time series data is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …