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针对公交车客流预测中时空依赖关系难以有效利用的问题，提出一种基于多信息注意力机制的动态自适应对抗图卷积网络客流预测模型。首先，利用时间特征编码器捕获不同时段客流之间的相似性，引入公交车站点的兴趣点(point of interest, POI)信息以辅助模型捕捉更多的节点特征。其次，采用动态建模时空依赖关系的方法完成对非欧几里得关系的建模，利用SimAM注意力模块捕获不同站点客流数据之间的整体差异性。在真实公交车客流数据集上的实验结果表明，相比最优基线模型，所提模型在预测未来12个时间步时的平均MAE和RMSE分别降低了0.34和0.33,展现了其在公交车客流预测中的有效性和优越性。
Abstract：Aiming at the difficulty of utilizing spatiotemporal dependence relationship in bus passenger flow prediction effectively, a prediction model of passenger flow based on multiple information attention and dynamic adaptive adversarial graph convolutional network was proposed. Firstly, the time feature encoder was used to capture the similarity between passenger flows at different time periods, and point of interest(POI) information of bus stations was incorporated to enhance node feature extraction. Secondly, the dynamic modeling of spatiotemporal dependence was adopted to complete the modeling of non-Euclidean relationships, and the SimAM attention module was utilized to capture the overall differences in passenger flow data at different stations. The experimental results on real bus passenger flow data showed that compared with the best baseline model, the proposed model reduced the average MAE and RMSE of the next 12 time steps by 0.34 and 0.33, respectively, demonstrating its effectiveness and superiority in predicting bus passenger flow.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2024120
中图分类号:U495
引用信息:
[1]颜建强,赵仁琪,高原,等.基于多信息注意力对抗图卷积的公交车客流预测[J].郑州大学学报(理学版),2026,58(02):17-24.DOI:10.13705/j.issn.1671-6841.2024120.
2025-01-13
2025-01-13
2025-01-13