郑州大学学报（理学版）2026，Vol.58Issue(2)：17-24,8.DOI:10.13705/j.issn.1671-6841.2024120
基于多信息注意力对抗图卷积的公交车客流预测
Bus Passenger Flow Prediction Based on Multiple Information Attention and Adversarial Graph Convolution
颜建强 1赵仁琪 1高原 2曲博婷2
作者信息
- 1. 西北大学 信息科学与技术学院 陕西 西安 710127
- 2. 西北大学 经济与管理学院 陕西 西安 710127
- 折叠
摘要
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 en-coder was used to capture the similarity between passenger flows at different time periods,and point of in-terest(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-Euclid-ean relationships,and the SimAM attention module was utilized to capture the overall differences in pas-senger 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 pre-dicting bus passenger flow.关键词
智能公交/客流预测/图卷积网络/注意力机制/时空依赖Key words
intelligent public transportation/passenger flow prediction/graph convolution network/at-tention mechanism/spatiotemporal dependence分类
信息技术与安全科学引用本文复制引用
颜建强,赵仁琪,高原,曲博婷..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(2):17-24,8.
