[1]钱汉强,时 玥,陈艳艳,等.考虑时间周期的图神经网络地铁客流预测[J].长安大学学报(自然科学版),2025,45(2):154-164.[doi:10.19721/j.cnki.1671-8879.2025.02.013]
　QIAN Han-qiang,SHI Yue,CHEN Yan-yan,et al.Graph neural network-based subway passenger flow prediction considering time periods[J].Journal of Chang’an University (Natural Science Edition),2025,45(2):154-164.[doi:10.19721/j.cnki.1671-8879.2025.02.013]
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考虑时间周期的图神经网络地铁客流预测()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第45卷
期数:
2025年2期
页码:
154-164
栏目:
交通工程
出版日期:
2025-03-31

文章信息/Info

Title:
Graph neural network-based subway passenger flow prediction considering time periods
文章编号:
1671-8879(2025)02-0154-11
作者:
钱汉强1; 2; 时 玥1; 2; 陈艳艳1; 2; 王嘉晨3
(1. 北京工业大学 城市交通学院,北京 100124; 2. 北京工业大学 交通工程北京市重点实验室,北京 100124; 3. 布朗大学 工程学院,罗德岛 普罗维登斯 02912)
Author(s):
QIAN Han-qiang1; 2;  SHI Yue1; 2;  CHEN Yan-yan1; 2;  WANG Jia-chen3
(1. School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; 2. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China; 3. School of Engineering, Brown University, Providence 02912, Rhode Island, USA)
关键词:
交通工程;  客流预测;  图神经网络;  地铁;  深度学习
Keywords:
traffic engineering;  passenger flow prediction;  graph neural network;  subway;  deep learning
分类号:
U491.14
DOI:
10.19721/j.cnki.1671-8879.2025.02.013
文献标志码:
A
摘要:
为了准确预测地铁进出站客流,提出一种基于深度学习算法的图神经时空网络(GNSTNet)地铁客流预测模型,该模型以历史24 h地铁进出站客流、天气和时间标签数据为输入,预测未来1 h内全网每个站点的进出站客流量,通过逐小时站点进出站客流和地铁网络邻接矩阵构建地铁客流时空图; 在GNSTNet模型中,利用图神经网络提取每一时间步的空间维度特征,通过傅里叶变换提取时间序列潜在周期性,并使用卷积神经网络提取时间维度的特征。研究结果表明:在北京地铁2021年6月的数据集中,基于图神经网络的地铁客流预测模型取得了比6种基准模型更高的预测精度; 相较于精度最高的基准模型,平均绝对误差平均减少了14.97%,均方根误差平均减少了13.35%; 基于图神经网络的地铁客流预测模型通过融合图神经网络、傅里叶变换和卷积神经网络,相较于传统算法有效地提升了对于全网地铁进出站客流量的预测精度。
Abstract:
To achieve precise subway passenger entry and exit flows prediction, a deep learning-based for subway passenger flow pediction graph neural spatio-temporal network(GNSTNet), was proposed. Historical 24-hour subway entry and exit flow data, weather and time labels were taken into account as inputs, and they were used to predict the hourly passenger entry and exit flows for every station in the entire network for the next hour. A subway passenger flow spatio-temporal graph were constructed using the hourly station entry and exit flows and subway network adjacency matrix. In the GNSTNet model, a graph neural network was used to extract the spatial features at each time step. The Fourier transform was used to extract the potential periodicity in the time series, and a convolutional neural network was used to extract the temporal features. The research results show that on a dataset from the Beijing Subway in June 2021, the graph neural network-based subway passenger flow prediction model outperforms six benchmark models, with an average reduction of 14.97% in the mean absolute error and 13.35% in the root mean squared error compared to the most accurate benchmark model. The graph neural networks-based subway passenger flow prediction model effectively improves the accuracy of predicting the passenger entry and exit flow for the entire subway network compared to traditional algorithms by integrating graph neural network, Fourier transform and convolutional neural network.2 tabs, 9 figs, 25 refs.

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备注/Memo

备注/Memo:
收稿日期:2024-08-18
基金项目:北京市自然科学基金项目(L241079)
作者简介:钱汉强(1997-),男,北京市人,工学博士研究生,E-mail:qianhq@emails.bjut.edu.cn。
通信作者:陈艳艳(1970-),女,河南郑州人,教授,博士研究生导师,E-mail:cdyan@bjut.edu.cn。
更新日期/Last Update: 2025-04-01