Dynamic ensemble multi-strategy based bald eagle search optimization algorithm: A controller parameters tuning approach

Liu, Ying; Li, Gongfa; Jiang, Du; Yun, Juntong; Huang, Li; Xie, Yuanmin; Jiang, Guozhang; Kong, Jianyi; Tao, Bo; Zou, Chunlong; Fang, Zifan

doi:10.1016/j.asoc.2023.110881

Cited by 2 publications

(1 citation statement)

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“…Metro development has played an important role in easing road traffic congestion, facilitating resident travel, and reducing carbon emissions 4,5 . With advancements in rail transit, metro passenger flow forecasting has received increasing attention 6 . Accurate passenger flow forecasting can provide scientific evidence for rail managers, predict passenger flow congestion in advance, and provide passengers with more travel options, thus reducing the number of transfers and shortening travel time 7 .…”

Section: Introductionmentioning

confidence: 99%

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

Xi,

Yan,

Jia

et al. 2024

Concurrency and Computation

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SummaryPassenger flow prediction is an important part of daily metro operation, and its accuracy affects the deployment of train resources management. Due to the complex spatiotemporal correlation characteristics of metro passenger flow, it is necessary to describe it to improve the accuracy of passenger flow prediction. However, the existing models mainly construct the weight matrix based on the static graph and the similarity between stations when describing the spatial correlation of station passenger flow but ignore the time‐varying characteristics of the spatial correlation of station passenger flow. To address this problem, this study introduces a dynamic multi‐graph and multidimensional attention spatiotemporal model. Specifically, the Graph Convolutional Neural Network combined with dynamic multigraph extracts spatial features and the Gated Recurrent Unit extracts temporal features of passenger flow. The multidimensional attention can obtain the spatiotemporal correlation of passenger flow data by assigning weights to them. Finally, this model has been used to conduct experiments on Beijing metro passenger flow datasets with time granularity of 10 and 15 min. The result indicates that the DGMANN model outperforms state‐of‐the‐art other deep learning methods in passenger flow prediction. In addition, the effectiveness of its key submodules has been verified through ablation experiments.

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Section: Introductionmentioning

confidence: 99%