利用图网络对多样化边缘物联网数据进行流量预测

Tao Shen, Lu Zhang, Renkang Geng, Shuai Li, Bin Sun
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引用次数: 0

摘要

越来越多的研究人员为物联网(IoT)设备提出人工智能算法,并将其应用于智慧城市和智能交通等主题。近年来,相关研究主要集中在数据处理和算法建模方面,大多数研究都取得了良好的预测效果。然而,许多算法模型往往会针对相应的数据集调整参数,因此模型的鲁棒性较弱。当不同类型的数据面对其他模型参数时,预测性能往往会有很大差异。因此,这项工作从数据处理和算法模型的角度入手。以交通数据为例,我们首先提出一种新的数据处理方法,将具有不同属性和特征的交通数据处理成对大多数模型来说更常见的数据集。然后,我们将从多个模型参数的角度对不同类型的数据集进行比较,并进一步分析不同交通数据在机器学习中的注意事项和变化趋势。最后,探讨不同类型的数据和模型参数范围,以及数据参数变化时预测结果波动的可能原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic prediction for diverse edge IoT data using graph network
More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.
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