Expressway Traffic Flow Prediction Based on Data from Multiple Related Toll Stations

Authors

ZHANG Yang, School of Transportation ， Fujian University of Technology ， Fuzhou ， Fujian 350118 ， ChinaFollow
YAO Fangyu, School of Transportation ， Fujian University of Technology ， Fuzhou ， Fujian 350118 ， China
YANG Shumin, School of Transportation Engineering ， Tongji University ， Shanghai 201804 ， China

Abstract

The strong inter-economic connection makes a spatial correlation between traffic data from multiple related toll stations between urban cluster regions，and an accurate description of this connection can improve the accuracy of expressway traffic flow prediction.However，due to many uncertainties，the correlation is difficult to be captured and quantified.To solve this problem，an ATGCN-ResGRU deep learning-based expressway traffic flow prediction method was proposed.By combining attention mechanisms，three graph convolutional networks （GCN ） topological networks with high，medium，and low attention levels were constructed，and spatial learning data was obtained according to the weighted attention level of each network.The connection of multiple related toll stations was quantified and graded.At the same time，to avoid the over-smoothing problem，two gated recurrent unit （GRU ） modules were connected by residuals to enhance the algorithm's ability to capture time regularity.Finally，a feature fusion layer and a fully connected layer were used to output the predicted values.This algorithm was used to predict the traffic flow at a expressway toll station in Guangdong Province，and the experimental results show that the method proposed in this paper can effectively improve the prediction accuracy.Compared with the classical models of diverse ensemble CNN-LSTM，CNN-BiLSTM，and DL-SVR，the mean absolute error （EMAE） is reduced by 7.95，4.52，and 12.88，and the root means square error （ERMSE） is reduced by 12.03，6.12，and 19.05.

Publication Date

3-18-2024

DOI

10.14048/j.issn.1671-2579.2024.01.027

First Page

202

Last Page

208

Submission Date

February 2025

Recommended Citation

Yang, ZHANG; Fangyu, YAO; and Shumin, YANG (2024) "Expressway Traffic Flow Prediction Based on Data from Multiple Related Toll Stations," Journal of China & Foreign Highway: Vol. 44: Iss. 1, Article 27.
DOI: 10.14048/j.issn.1671-2579.2024.01.027
Available at: https://zwgl1980.csust.edu.cn/journal/vol44/iss1/27

Reference

[1]RUTKA G .Network traffic prediction using ARIMA and neural networks models [J].Elektronika IR Elektrotechnika ,2008（4）:47-52.
[2]XU D W ,WANG Y D,JIA L M ,et al .Real-time road traffic state prediction based on ARIMA and Kalman filter [J].Frontiers of Information Technology & Electronic Engineering ,2017 ,18(2): 287-302.
[3]殷 礼 胜 ,魏 帅 康 ,孙 双 晨 ,等.基 于 FEEMD-SAPSO-BiLSTM 组合模型的短时交通流预测 [J].电子测量与仪器学报 ,2021 ,35(10):72-81.YIN Lisheng ,WEI Shuaikang ,SUN Shuangchen ,et al .Short-term traffic flow forecast based on FEEMD-SAPSO-BiLSTM combined model [J].Journal of Electronic Measurement and Instrumentation ,2021 ,35(10):72-81.
[4]SUN P ,BOUKERCHE A ,TAO Y J .SSGRU :A novel hybrid stacked GRU-based traffic volume prediction approach in a road network [J].Computer Communications ,2020 ,160:502-511.
[5]DAI G W ,MA C X ,XU X C .Short-term traffic flow prediction method for urban road sections based on space-time analysis and GRU [J].IEEE Access ,2019 ,7:143025 -143035 .
[6]NIU K ,CHENG C ,CHANG J L ,et al .Real-time taxi-passenger prediction with L-CNN [J].IEEE Transactions on Vehicular Technology ,2019 ,68(5):4122 -4129 .
[7]ZHANG W B ,YU Y H ,QI Y ,et al .Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning [J].Transportmetrica A-Transport Science ,2019 ,15(2):1688 -1711 .
[8]WANG X ,AN K ,TANG L ,et al .Short term prediction of freeway exiting volume based on SVM and KNN [J].International Journal of Transportation Science and Technology ,2015 ,4(3):337-352.
[9]HAN D X ,CHEN J ,SUN J .A parallel spatiotemporal deep learning network for highway traffic flow forecasting [J].International Journal of Distributed Sensor Networks ,2019 ,15(2):155014771983279 .
[10]JIN C H ,RUAN T ,WU D X ,et al .HetGAT :A heterogeneous graph attentio n network for freeway traffic speed prediction [J].Journal of Ambient Intelligence and Humanized Computing ,2021 :2021 ,10(3):1-12.
[11]陆由付 ,樊振通 ,韩冰 ,等.基于 GA-ELM 的水性环氧乳化沥青黏结性能预测 [J].中外公路 ,2021 ,41(4):336-340.LU Youfu ,FAN Zhentong ,HAN Bing ,et al .Prediction of bonding performance of water-borne epoxy emulsified asphalt based on GA-ELM [J].Journal of China & Foreign Highway ,2021 ,41(4):336-340.
[12]张丽娟 ,杨源 ,梅诚 ,等.基于 ARIMA 和SVM 模型的沥青路面使用性能预测比较 [J].中外公路 ,2022 ,42(2):52-56.ZHANG Lijuan ,YANG Yuan ,MEI Cheng ,et al .Comparing predictions of asphalt pavement performance between ARIMA and SVM models [J].Journal of China & Foreign Highway ,2022 ,42(2):52-56.
[13]李莹 ,李晓霞 .基于深度学习的短时交通流预测 [J].公路工程 ,2021 ,46(3):314-319.LI Ying ,LI Xiaoxia .Short-term traffic flow prediction based on deep learning [J].Highway Engineering ,2021 ,46(3):314-319.
[14]ZHANG Y ,XIN D R .A diverse ensemble deep learning method for short-term traffic flow prediction based on spatiotemporal correlations [J].IEEE Transactions on Intelligent Transportation Systems ,2022 ,23(9):16715 -16727 .
[15]ZHUANG W Q ,CAO Y B .Short-term traffic flow prediction based on CNN-BILSTM with multicomponent information [J].Applied Sciences ,2022 ,12(17):8714 .
[16]傅成红 ,杨书敏 ,张阳 .改进支持向量回归机的短时交通流 预 测 [J].交 通 运 输 系 统 工 程 与 信 息 ,2019 ,19(4):130-134,148.FU Chenghong ,YANG Shumin ,ZHANG Yang .Promoted short-term traffic flow prediction model based on deep learning and support vector regression [J].Journal of Transportation Systems Engineering and Information Technology ,2019 ,19(4):130-134,148.