Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity …
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
Q Ren, Y Li, Y Liu - Expert Systems with Applications, 2023 - Elsevier
Abstract Recently, Temporal Convolution Networks (TCNs) and Graph Convolution Network (GCN) have been developed for traffic forecasting and obtained promising results as their …
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for enabling advanced transportation management and services to address worsening traffic …
X Ouyang, Y Yang, Y Zhang, W Zhou, J Wan… - Knowledge-Based …, 2023 - Elsevier
Deep learning models have emerged as a promising way for traffic prediction. However, the requirement for large amounts of training data remains a significant issue for achieving well …
Y Jin, K Chen, Q Yang - Proceedings of the 29th ACM SIGKDD …, 2023 - dl.acm.org
Graph-based deep learning models are powerful in modeling spatio-temporal graphs for traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data …
Y Jin, K Chen, Q Yang - Proceedings of the 28th ACM SIGKDD …, 2022 - dl.acm.org
Deep learning models have been demonstrated powerful in modeling complex spatio- temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on …
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …