Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction

IF 2 4区 工程技术 Q2 ENGINEERING, CIVIL Journal of Advanced Transportation Pub Date : 2024-12-07 DOI:10.1155/atr/7109780
Lei Huang, Jianxin Qin, Tao Wu
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Abstract

With the rapid development of transport technology and the increasing complexity of traffic patterns, integrating multiple data sources for traffic flow prediction has become crucial to overcome the defects of a single data source. This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. In addition, a GCN combined with gated recurrent units (GRUs) is proposed for spatiotemporal modeling of data fusion and traffic flow prediction. The main contributions are: (1) The approach significantly improved prediction accuracy by leveraging multiple data sources compared to a single source. (2) A unified graph structure was created via global traffic nodes to interpolate traffic flow and address data sparsity. (3) The proposed model demonstrates an over 11% improvement in accuracy compared to other baseline models, as measured by the weighted mean absolute percentage error (WMAPE). It also exhibits stability in multitime scale predictions, highlighting the effectiveness of multisource data fusion, data imputation, and node-level prediction capabilities. The approach provides valuable insights for managing urban traffic data from multiple sources and predicting traffic flow, and it shows stability in multitime scale predictions.

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基于图卷积神经网络的多源数据融合节点级交通流预测
随着交通技术的飞速发展和交通模式的日益复杂,整合多个数据源进行交通流预测已成为克服单一数据源缺陷的关键。介绍了一种基于图卷积神经网络(GCNs)的多源数据融合方法,用于节点级交通流预测。具体来说,它从多个数据源中提取不同类型的交通流,并利用全局交通节点对交通流进行插值,构建统一的图结构。此外,提出了一种结合门控循环单元(gru)的GCN算法,用于数据融合的时空建模和交通流预测。主要贡献有:(1)与单一数据源相比,该方法通过利用多个数据源显著提高了预测精度。(2)通过全局交通节点构建统一的图结构,对交通流进行插值,解决数据稀疏性问题。(3)加权平均绝对百分比误差(WMAPE)表明,与其他基线模型相比,该模型的精度提高了11%以上。它在多时间尺度预测中也表现出稳定性,突出了多源数据融合、数据输入和节点级预测能力的有效性。该方法为管理来自多个来源的城市交通数据和预测交通流量提供了有价值的见解,并且在多时间尺度预测中显示出稳定性。
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来源期刊
Journal of Advanced Transportation
Journal of Advanced Transportation 工程技术-工程:土木
CiteScore
5.00
自引率
8.70%
发文量
466
审稿时长
7.3 months
期刊介绍: The Journal of Advanced Transportation (JAT) is a fully peer reviewed international journal in transportation research areas related to public transit, road traffic, transport networks and air transport. It publishes theoretical and innovative papers on analysis, design, operations, optimization and planning of multi-modal transport networks, transit & traffic systems, transport technology and traffic safety. Urban rail and bus systems, Pedestrian studies, traffic flow theory and control, Intelligent Transport Systems (ITS) and automated and/or connected vehicles are some topics of interest. Highway engineering, railway engineering and logistics do not fall within the aims and scope of JAT.
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