A Hybrid Neural Network for the Traffic Flow Prediction on the Premise of Missing Data

IF 2.5 4区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Intelligent Transport Systems Pub Date : 2025-08-14 DOI:10.1049/itr2.70070

Junxi Chen, Zhenlin Wei, Jiaxin Zhang

引用次数: 0

Abstract

On the basis of a generative adversarial network (GAN) and a convolutional neural network (CNN), this work proposes an ER-GAN-CNN to forecast the traffic flow in the presence of missing data by improving GAN. Due to the occurrence of emergencies and the fault of the relevant equipment, the equipment for passenger flow detection may lose some data, which would have negative impacts on passenger flow prediction. In order to cope with such situations, GAN is introduced to make up for the missing data in this paper. On the basis of complete data and CNN, inception blocks are built thereafter to further predict the passenger flow/traffic flow. The accuracy of the prediction of passenger flow is significantly improved with the help of the ER-GAN-CNN, which is able to provide more accurate and rapid traffic guidance for the drivers.

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基于缺失数据的混合神经网络交通流预测

在生成对抗网络（GAN）和卷积神经网络（CNN）的基础上，本文提出了一种ER-GAN-CNN，通过改进GAN来预测数据缺失情况下的交通流量。由于突发事件的发生和相关设备的故障，客流检测设备可能会丢失一些数据，从而对客流预测产生负面影响。为了应对这种情况，本文引入GAN来弥补数据缺失。在完整数据和CNN的基础上，构建初始块，进一步预测客流/交通流。在ER-GAN-CNN的帮助下，客流预测的准确性显著提高，能够为驾驶员提供更准确、更快速的交通引导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Intelligent Transport Systems 工程技术-运输科技

CiteScore

6.50

自引率

7.40%

发文量

159

审稿时长

3 months

期刊介绍： IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf