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A Deep Neural Network Based on Classification of Traffic Volume for Short-Term Forecasting

机译：基于交通量分类的深度神经网络用于短期预测

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摘要

This paper developed a deep architecture to predict the short-term traffic flow in an urban traffic network. The architecture consists of three main modules: a pretraining module, which generates initialized weights and provides a rough learning of the features firstly with the training set in an unsupervised manner; a classification module, which performs the data classification operation through adding the logistic regression on top of the pretrained architecture to distinguish the traffic state; and a fine-tuning module, which predicts the traffic flow with supervised training based on the initialized weights in the first module. The classification module provides the fine-tuning modules with two classified datasets for more accurate forecasting. Furthermore, both upstream and downstream data are utilized to improve the prediction performance. The effectiveness of the proposed model was verified by the traffic prediction of the road segments of Nanming District of Guiyang. And with the comparison analysis over the existing approaches, the proposed model shows superiority in short-term traffic prediction, especially under incident conditions.
机译：本文开发了一种深度架构，可预测城市交通网络中的短期交通流量。该体系结构由三个主要模块组成：一个预训练模块，该模块生成初始化的权重，并首先以无监督的方式使用训练集对特征进行粗略的学习；分类模块，其通过在预训练的体系结构的顶部添加逻辑回归来区分交通状况来执行数据分类操作。以及微调模块，其基于第一模块中的初始权重，通过监督训练来预测交通流量。分类模块为微调模块提供两个分类的数据集，以进行更准确的预测。此外，上游和下游数据均被用来改善预测性能。贵阳市南明区路段交通预测对所提模型的有效性进行了验证。并通过与现有方法的比较分析，提出的模型显示出在短期交通预测中的优越性，尤其是在事故情况下。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第4期|6318094.1-6318094.10|共10页
  • 作者

    Bai Jing; Chen Yehua;

  • 作者单位

    Yanshan Univ, Sch Econ & Management, Qinhuangdao 066004, Peoples R China;

    Yanshan Univ, Sch Econ & Management, Qinhuangdao 066004, Peoples R China;

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