Prediction of Highway Traffic Volume Based on Markov
Chain-BP Neural Network Model

doi:10.3969/j.issn.1674-0696.2021.02.06

Journal of Chongqing Jiaotong University(Natural Science) ›› 2021, Vol. 40 ›› Issue (02): 35-41.DOI: 10.3969/j.issn.1674-0696.2021.02.06

• Transport+Big Data and Artificial Intelligence • Previous Articles Next Articles

Prediction of Highway Traffic Volume Based on Markov Chain-BP Neural Network Model

PEI Tongsong1, PEI Yu2

(1. Department of Electrical and Information Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050091, Hebei, China; 2. Department of Civil Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050091, Hebei, China)

Received:2019-03-13 Revised:2019-07-04 Online:2021-02-16 Published:2021-02-16

基于马尔科夫链-BP神经网络模型对公路运量的预测研究

裴同松1，裴彧2

(1. 河北交通职业技术学院电气与信息工程系,河北石家庄 050091； 2. 河北交通职业技术学院土木工程系,河北石家庄 050091)

作者简介:裴桐松（1965—），男，河北石家庄人，副教授，硕士，主要从事交通工程方面的研究。E-mail:Pts8888@126.com 通信作者：裴彧（1993—），男，河北石家庄人，硕士，主要从事道路桥梁工程方面的研究。E-mail:peiyu0918@126.com
基金资助:
河北省高校百名优秀人才计划项目（BR206）;河北省科技基金项目（16217604D）；2019年河北省高等学校科学技术研究项目（Z2019067）

Abstract

Abstract: Taking the highway traffic volume data of a region in Hebei Province from 1998 to 2017 as an example, BP neural network model was used to predict and Markov chain was used to correct the predicted value. The actual value of highway traffic volume was compared with the predicted value of BP neural network and the modified value of Markov chain, and the highway traffic volume data from 2018 to 2019 was predicted. The BP neural network prediction model modified by Markov chain can reduce the average relative error of highway passenger volume and freight volume to 2.07% and 2.14%, respectively. The modified model can not only accurately predict the road traffic volume, but also provide favorable opinions for the future development of highway transportation.

Key words: traffic and transportation engineering, road traffic volume, Markov chain, BP neural network, prediction

摘要： 选取河北省某地区1998—2017年公路运量数据为例，采用BP神经网络模型进行预测并用马尔科夫链修正预测值，将公路运量实际值与BP神经网络预测值及马尔科夫链修正值作对比分析并预测了2018—2019年的公路运量数据。使用马尔科夫链修正后的BP神经网络预测模型可以将公路客运量和货运量的平均相对误差分别下降至2.07%和2.14%。修正后的模型不仅可以准确的对公路运量做出预测，而且可以为未来公路运输发展提供有利意见。

关键词: 交通运输工程, 公路运量, 马尔科夫链, BP神经网络, 预测

CLC Number:

U491.1+4

PEI Tongsong1, PEI Yu2. Prediction of Highway Traffic Volume Based on Markov Chain-BP Neural Network Model[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(02): 35-41.

裴同松1，裴彧2. 基于马尔科夫链-BP神经网络模型对公路运量的预测研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(02): 35-41.

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Prediction of Highway Traffic Volume Based on Markov Chain-BP Neural Network Model

基于马尔科夫链-BP神经网络模型对公路运量的预测研究

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