{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T03:15:51Z","timestamp":1780370151687,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["3132024302"],"award-info":[{"award-number":["3132024302"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["21YJC630066"],"award-info":[{"award-number":["21YJC630066"]}]},{"name":"Humanities and Social Sciences Foundation of Ministry of Education","award":["3132024302"],"award-info":[{"award-number":["3132024302"]}]},{"name":"Humanities and Social Sciences Foundation of Ministry of Education","award":["21YJC630066"],"award-info":[{"award-number":["21YJC630066"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>The rapid growth in the number of motor vehicles has exacerbated traffic congestion. The occurrence of congestion not only poses significant challenges for traffic management authorities but also severely impacts residents\u2019 travel and daily routines. Against this backdrop, predicting traffic flow can provide crucial insights for anticipating changing traffic patterns. Therefore, this paper proposes a novel hybrid deep learning architecture (CNN-LSTM-GRU) for highway traffic flow prediction that integrates spatiotemporal and meteorological dimensions. Our approach constructs a multidimensional feature matrix encompassing temporal sequences, spatial correlations, and weather conditions. Convolutional Neural Networks (CNN) are employed to capture spatial patterns, while Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks jointly model temporal dependencies. Through systematic hyperparameter tuning and step-length optimization, we validate the model using real-world traffic data from a provincial highway network. The experimental evaluation analyzes the following two critical dimensions: (1) holiday vs. non-holiday traffic patterns, and (2) the impact of weather data integration. Comparative analysis reveals that our hybrid model demonstrates superior prediction accuracy over standalone LSTM, GRU, and their CNN-based counterparts (CNN-LSTM, CNN-GRU).<\/jats:p>","DOI":"10.3390\/systems13090765","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T14:16:55Z","timestamp":1756822615000},"page":"765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A CNN-LSTM-GRU Hybrid Model for Spatiotemporal Highway Traffic Flow Prediction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6269-9115","authenticated-orcid":false,"given":"Jinsong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Sha","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Dalian Minzu University, Dalian 116650, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.trc.2014.02.006","article-title":"Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification","volume":"43","author":"Guo","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.trb.2004.03.003","article-title":"Real-time freeway traffic state estimation based on extended Kalman filter: A general approach","volume":"39","author":"Wang","year":"2005","journal-title":"Transp. Res. Part B"},{"key":"ref_3","first-page":"1","article-title":"Analysis of freeway traffic time-series data by using Box-Jenkins techniques","volume":"722","author":"Ahmed","year":"1979","journal-title":"Transp. Res. Rec."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0968-090X(02)00009-8","article-title":"Comparison of parametric and nonparametric models for traffic flow forecasting","volume":"10","author":"Smith","year":"2002","journal-title":"Transp. Res. Part C"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12544-015-0170-8","article-title":"Short-term traffic flow prediction using seasonal ARIMA model with limited input data","volume":"7","author":"Kumar","year":"2015","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_6","first-page":"1177","article-title":"Traffic network flow forecasting based on switching model","volume":"24","author":"Chen","year":"2009","journal-title":"Control Decis."},{"key":"ref_7","first-page":"91","article-title":"Accurate Multisteps Traffic Flow Prediction Based on SVM","volume":"12","author":"Zhang","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/TITS.2014.2371993","article-title":"Traffic Flow Forecasting for Urban Work Zones","volume":"16","author":"Hou","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","first-page":"376","article-title":"K-NN based nonparametric regression method for short-term traffic flow forecasting","volume":"30","author":"Zhang","year":"2010","journal-title":"Syst. Eng. Theory Pract."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1080\/19427867.2024.2339631","article-title":"Traffic flow prediction for highway vehicle detectors through decomposition and machine learning","volume":"17","author":"Lu","year":"2025","journal-title":"Transp. Lett. Int. J. Transp. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","article-title":"Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks","volume":"259","author":"Zhang","year":"2017","journal-title":"Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C"},{"key":"ref_13","unstructured":"Wu, Y., and Tan, H. (2016). Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1109\/TITS.2018.2843349","article-title":"An evaluation of HTM and LSTM for short-term arterial traffic flow prediction","volume":"20","author":"Mackenzie","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.neucom.2018.08.067","article-title":"LSTM-based traffic flow prediction with missing data","volume":"318","author":"Tian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1109\/TITS.2018.2854913","article-title":"Adaptive Multi-Kernel SVM With Spatial-Temporal Correlation for Short-Term Traffic Flow Prediction","volume":"20","author":"Feng","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3913","DOI":"10.1109\/TITS.2019.2906365","article-title":"Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting","volume":"20","author":"Guo","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","unstructured":"Li, Y., Yu, R., Shahabi, C., and Liu, Y. (May, January 30). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fang, W., Zhuo, W., Yan, J., Song, Y., Jiang, D., and Zhou, T. (2022). Attention meets long short-term memory: A deep learning network for traffic flow forecasting. Phys. A Stat. Mech. Its Appl., 587.","DOI":"10.1016\/j.physa.2021.126485"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shuai, C., Pan, Z., Gao, L., and Zuo, H. (2021). Short-term traffic flow prediction of expressway: A hybrid method based on singular spectrum analysis decomposition. Adv. Civ. Eng., 2021.","DOI":"10.1155\/2021\/4313970"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bing, Q., Shen, F., Chen, X., Zhang, W., Hu, Y., and Qu, D. (2021). A hybrid short-term traffic flow multistep prediction method based on variational mode decomposition and long short-term memory model. Discret. Dyn. Nat. Soc., 2021.","DOI":"10.1155\/2021\/4097149"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, X., Liu, C., Zhao, Y., and Lv, X. (2022). Short-term traffic flow prediction based on whale optimization algorithm optimized BiLSTM_Attention. Concurr. Comput. Pract. Exp., 34.","DOI":"10.1002\/cpe.6782"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Dong, C., Dong, D., and Wang, S. (2021). Traffic volume prediction: A fusion deep learning model considering spatial\u2013temporal correlation. Sustainability, 13.","DOI":"10.3390\/su131910595"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6910","DOI":"10.1109\/TITS.2020.2997352","article-title":"A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction","volume":"22","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","first-page":"87","article-title":"Short-time traffic flow prediction based on a hybrid model of Bi-LSTM-CNN","volume":"65","author":"Lian","year":"2025","journal-title":"Adv. Transp. Stud."},{"key":"ref_26","unstructured":"Ding, R., Xie, H., Dai, C., and Qiao, G. (2023, January 22\u201324). Research on ship traffic flow prediction based on GTO-CNN-LSTM. Proceedings of the International Conference on Traffic Engineering and Transportation System, Dalian, China."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jia, Y., Wu, J., and Xu, M. (2017). Traffic flow prediction with rainfall impact using a deep learning method. J. Adv. Transp., 1.","DOI":"10.1155\/2017\/6575947"},{"key":"ref_28","first-page":"126","article-title":"Traffic flow forecasting method based on Gated Spatial-temporal Spatiotemporal Graph Network and TCN","volume":"6","author":"Huang","year":"2024","journal-title":"J. Traffic Sci. Technol."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/765\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:37:12Z","timestamp":1760035032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/765"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":28,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["systems13090765"],"URL":"https:\/\/doi.org\/10.3390\/systems13090765","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]}}}