A novel spatio-temporal feature interleaved contrast learning neural network from a robustness perspective DOI
Peng Liu, Yaodong Zhu, Yang Yang

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: unknown, P. 112788 - 112788

Published: Nov. 1, 2024

Language: Английский

Traffic Flow Prediction Framework That Can Appropriately Process the Noise, Volatility, and Nonlinearity in Traffic Flow Data DOI Creative Commons

Yingping Tang,

Qiang Shang,

Longjiao Yin

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of prediction, we developed a framework—namely, multicomponent network—that appropriately processes noise, volatility, and nonlinearity in data. This framework comprises three components: factor selection component, decomposition component. The component considers dynamic effects weather‐related, environmental, spatiotemporal factors on flow; it then extracts analyzes exhibiting strong correlations with flow. optimizes parameters variational mode basis envelope entropy by using sparrow search algorithm; transforms into multiple intrinsic functions to enable accurate prediction. Finally, constructs feature matrices bidirectional gated recurrent unit model identify relationships within Moreover, uses an attention mechanism assign different weights features importance these thereby enabling efficient processing large volume performance proposed was examined experiments conducted volumes data time granularities. results indicated that achieved high stability various granularities, samples, dataset sizes, noise conditions. generally outperformed existing models under all experimental

Language: Английский

Citations

0
Emerging Trends in Graph Neural Networks for Traffic Flow Prediction: A Survey DOI

Guangrui Fan,

Aznul Qalid Md Sabri,

Siti Soraya Abdul Rahman

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

0
Traffic Forecasting with Meta Attentive Graph Convolutional Recurrent Network DOI
Adnan Zeb, Jianying Zheng, Yongchao Ye

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128073 - 128073

Published: May 1, 2025

Language: Английский

Citations

0
A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Spatio-Temporal Deep Learning with Considering Associated Factors Selection DOI Creative Commons

Yingping Tang,

Qiang Shang,

Longjiao Yin

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 128215 - 128234

Published: Jan. 1, 2024

Language: Английский

Citations

0
Informer-FDR: A short-term vehicle speed prediction model in car-following scenario based on traffic environment DOI

Qifan Xue,

Jian Ma, Xuan Zhao

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 262, P. 125655 - 125655

Published: Nov. 2, 2024

Language: Английский

Citations

0
A novel spatio-temporal feature interleaved contrast learning neural network from a robustness perspective DOI
Peng Liu, Yaodong Zhu, Yang Yang

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: unknown, P. 112788 - 112788

Published: Nov. 1, 2024

Language: Английский

Citations

0