Multivariate long-time series traffic passenger flow prediction using causal convolutional sparse self-attention MTS-Informer

• Published in: Neural computing & applications Vol. 35; no. 34; pp. 24207 - 24223
• Main Authors: Liu, Miaonan, Wang, Wei, Hu, Xianhui, Fu, Yunlai, Xu, Fujin, Miao, Xinying
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Flow distribution; Flow stability; Forecasting; Image Processing and Computer Vision; Intelligent transportation systems; Machine learning; Methods; Neural networks; Operation scheduling; Original Article; Passengers; Prediction models; Probability and Statistics in Computer Science; R&D; Research & development; Standard error; Time series; Traffic flow; Traffic models; Transportation planning; Trends; Urban rail; Wavelet transforms; Stationarity detection; Causal convolutional networks; Traffic passenger flow prediction; Informer

As an important part of the operation preparation process of the intelligent transportation system, the passenger flow distribution law and forecast can guide the urban rail transit to formulate a reasonable operation scheduling plan. Due to the complexity, multi-variables, and instability of traffi...