{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:45:34Z","timestamp":1784216734971,"version":"3.55.0"},"reference-count":58,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T00:00:00Z","timestamp":1712448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Italian Ministry for Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered.<\/jats:p>","DOI":"10.3390\/s24072348","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T06:04:58Z","timestamp":1712556298000},"page":"2348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Proposal of a Machine Learning Approach for Traffic Flow Prediction"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6564-704X","authenticated-orcid":false,"given":"Mariaelena","family":"Berlotti","sequence":"first","affiliation":[{"name":"Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8895-2175","authenticated-orcid":false,"given":"Sarah","family":"Di Grande","sequence":"additional","affiliation":[{"name":"Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9077-3688","authenticated-orcid":false,"given":"Salvatore","family":"Cavalieri","sequence":"additional","affiliation":[{"name":"Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,7]]},"reference":[{"key":"ref_1","unstructured":"(2024, March 11). ONU World Population Prospects 2019: Highlights. Available online: https:\/\/population.un.org\/wpp\/publications\/files\/wpp2019_highlights.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7345","DOI":"10.1109\/JIOT.2020.2983089","article-title":"A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things","volume":"7","author":"Xu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/ACCESS.2016.2517076","article-title":"Managing Big City Information Based on WebVRGIS","volume":"4","author":"Lv","year":"2016","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27691","DOI":"10.1109\/ACCESS.2017.2778339","article-title":"Novel Cooperative and Fully-Distributed Congestion Control Mechanism for Content Centric Networking","volume":"5","author":"Ndikumana","year":"2017","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9413","DOI":"10.1109\/ACCESS.2017.2649567","article-title":"Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles","volume":"4","author":"Nie","year":"2016","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MITS.2017.2709881","article-title":"Lane-Based Saturation Degree Estimation for Signalized Intersections Using Travel Time Data","volume":"9","author":"Ma","year":"2017","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_7","first-page":"1","article-title":"Intelligent traffic flow prediction and analysis based on internet of things and big data","volume":"2022","author":"Liu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1049\/iet-its.2016.0233","article-title":"Traffic Demand Estimation for Lane Groups at Signal-Controlled Intersections Using Travel Times from Video-Imaging Detectors","volume":"11","author":"Ma","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_9","first-page":"89","article-title":"Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities","volume":"2","author":"Kotapati","year":"2023","journal-title":"Mechatron. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"211","DOI":"10.56578\/judm020404","article-title":"Integrating Machine Learning and Deep Learning in Smart Cities for Enhanced Traffic Congestion Management: An Empirical Review","volume":"2","author":"Ivan","year":"2023","journal-title":"J. Urban Dev. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.neucom.2017.06.017","article-title":"Efficient Traffic Congestion Estimation Using Multiple Spatio-Temporal Properties","volume":"267","author":"Yang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_12","unstructured":"French, S., Barchers, C., and Zhang, W. (2015, January 7\u201310). Moving beyond Operations: Leveraging Big Data for Urban Planning Decisions. Proceedings of the CUPUM 2015\u201414th International Conference on Computers in Urban Planning and Urban Management, Cambridge, MA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/mice.12140","article-title":"Demand Profiling for Dynamic Traffic Assignment by Integrating Departure Time Choice and Trip Distribution","volume":"31","author":"Levin","year":"2016","journal-title":"Comput. -Aided Civ. Infrastruct. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2015.12.013","article-title":"A Distributed Spatial\u2013Temporal Weighted Model on MapReduce for Short-Term Traffic Flow Forecasting","volume":"179","author":"Xia","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s11280-017-0487-4","article-title":"LoTAD: Long-Term Traffic Anomaly Detection Based on Crowdsourced Bus Trajectory Data","volume":"21","author":"Kong","year":"2018","journal-title":"World Wide Web"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.trc.2015.08.017","article-title":"Short-Term Traffic Flow Rate Forecasting Based on Identifying Similar Traffic Patterns","volume":"66","author":"Habtemichael","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Berlotti, M., Di Grande, S., Cavalieri, S., Torrisi, V., and Inturri, G. (2023, January 15\u201318). Proposal of an AI Based Approach for Urban Traffic Prediction from Mobility Data. Proceedings of the IEEE BigData2023\u20132023 IEEE International Conference on Big Data, Sorrento, Italy.","DOI":"10.1109\/BigData59044.2023.10386509"},{"key":"ref_18","first-page":"100739","article-title":"Urban Traffic Flow Prediction Techniques: A Review","volume":"35","author":"Sierra","year":"2022","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_19","first-page":"82","article-title":"A Summary of Traffic Flow Forecasting Methods","volume":"21","author":"Liu","year":"2004","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_20","unstructured":"Lin, S.-L., Huang, H.-Q., Zhu, D.-Q., and Wang, T.-Z. (2009, January 12\u201315). The Application of Space-Time ARIMA Model on Traffic Flow Forecasting. Proceedings of the ICMLC 2009\u2013International Conference on Machine Learning and Cybernetics, Baoding, China."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s12204-019-2152-9","article-title":"Traffic Prediction Method for GEO Satellites Combining ARIMA Model and Grey Model","volume":"25","author":"Zhou","year":"2020","journal-title":"J. Shanghai Jiaotong Univ. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"943","DOI":"10.4236\/ojbm.2020.82059","article-title":"Traffic Congestion and Duration Prediction Model Based on Regression Analysis and Survival Analysis","volume":"8","author":"Liu","year":"2020","journal-title":"Open J. Bus. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the NIPS 2017\u2014The 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_25","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased boostimg with categorical features. Proceedings of the NeurIPS 2018\u2014The 32nd Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"032098","DOI":"10.1088\/1742-6596\/1533\/3\/032098","article-title":"Time Series Prediction of Wireless Network Traffic Flow Based on Wavelet Analysis and BP Neural Network","volume":"1533","author":"Li","year":"2020","journal-title":"J. Phys.Conf. Ser."},{"key":"ref_27","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 Emerg. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.image.2016.06.007","article-title":"Spatial\u2013temporal convolutional neural networks for anomaly detection and localization in crowded scenes","volume":"47","author":"Zhou","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TII.2024.3363089","article-title":"Phase Space Graph Convolutional Network for Chaotic Time Series Learning","volume":"20","author":"Ren","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_31","first-page":"4173","article-title":"Modeling dynamic traffic flow as visibility graphs: A network-scale prediction framework for lane-level traffic flow based on LPR data","volume":"24","author":"Jie","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_32","first-page":"922","article-title":"Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting","volume":"33","author":"Guo","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_33","first-page":"869","article-title":"Full-Scale Spatio-Temporal Traffic Flow Estimation for City-Wide Networks: A Transfer Learning Based Approach","volume":"11","author":"Zhang","year":"2023","journal-title":"Transp. B Transp. Dyn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"75629","DOI":"10.1109\/ACCESS.2018.2879055","article-title":"Short-Term Traffic Flow Forecasting by Selecting Appropriate Predictions Based on Pattern Matching","volume":"6","author":"Ma","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","unstructured":"(2024, March 11). MOBILTRAF 300|Famas System. Available online: http:\/\/www.famassystem.it\/it\/prodotto\/mobiltraf-300."},{"key":"ref_36","first-page":"1","article-title":"Outlier Detection and Effects on Modeling","volume":"7","author":"Arimie","year":"2020","journal-title":"Open Access Libr. J."},{"key":"ref_37","first-page":"213","article-title":"A Comparison of the Outlier Detecting Methods: An Application on Turkish Foreign Trade Data","volume":"5","year":"2021","journal-title":"J. Math. Sci."},{"key":"ref_38","first-page":"1","article-title":"Time Series K-Means: A New k-Means Type Smooth Subspace Clustering for Time Series Data","volume":"367","author":"Huang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_39","unstructured":"(2024, March 11). Tslearn.Clustering.TimeSeriesKMeans\u2014Tslearn 0.6.3 Documentation. Available online: https:\/\/tslearn.readthedocs.io\/en\/stable\/gen_modules\/clustering\/tslearn.clustering.TimeSeriesKMeans.html."},{"key":"ref_40","unstructured":"(2024, March 11). Time Series Made Easy in Python\u2014Darts Documentation. Available online: https:\/\/unit8co.github.io\/darts\/."},{"key":"ref_41","unstructured":"(2024, March 11). Optuna: A Hyperparameter Optimization Framework. Available online: https:\/\/optuna.readthedocs.io."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ins.2011.12.028","article-title":"On the Use of Cross-Validation for Time Series Predictor Evaluation","volume":"191","author":"Bergmeir","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_43","first-page":"1742","article-title":"Grid Search of Multilayer Perceptron Based on the Walk-Forward Validation Methodology","volume":"11","author":"Ngoc","year":"2021","journal-title":"Int. J. Electr. Comput. Eng. IJECE"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Di Grande, S., Berlotti, M., Cavalieri, S., and Gueli, R. (2023, January 16\u201318). A Machine Learning Approach for Hydroelectric Power Forecasting. Proceedings of the IREC 2023\u2014The 14th International Renewable Energy Congress, Sousse, Tunisia.","DOI":"10.1109\/IREC59750.2023.10389561"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108148","DOI":"10.1016\/j.patcog.2021.108148","article-title":"Improving the Accuracy of Global Forecasting Models Using Time Series Data Augmentation","volume":"120","author":"Bandara","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1080\/01605682.2021.1892464","article-title":"On the Selection of Forecasting Accuracy Measures","volume":"73","author":"Koutsandreas","year":"2022","journal-title":"J. Oper. Res. Soc."},{"key":"ref_47","first-page":"45","article-title":"Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology","volume":"14","author":"Botchkarev","year":"2019","journal-title":"Interdiscip. J. Inf. Knowl. Manag. IJIKM"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another Look at Measures of Forecast Accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Abdurrahman, M.H., Irawan, B., and Setianingsih, C. (2020, January 28). A Review of Light Gradient Boosting Machine Method for Hate Speech Classification on Twitter. Proceedings of the ICECIE 2020\u2014The 2nd International Conference on Electrical, Control and Instrumentation Engineering, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICECIE50279.2020.9309565"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s10479-021-04187-w","article-title":"Forecasting gold price with the XGBoost algorithm and SHAP interaction values","volume":"334","author":"Jabeur","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103155","DOI":"10.1016\/j.autcon.2020.103155","article-title":"XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring","volume":"114","author":"Dong","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_52","first-page":"36","article-title":"A Scalable Tree Boosting System: XG Boost","volume":"7","author":"Mounika","year":"2020","journal-title":"Int. J. Res. Stud. Sci. Eng. Technol."},{"key":"ref_53","first-page":"5442","article-title":"Darts: User-Friendly Modern Machine Learning for Time Series","volume":"23","author":"Herzen","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: An interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A Random Forest Guided Tour","volume":"25","author":"Biau","year":"2016","journal-title":"TEST"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5176","DOI":"10.1038\/s41598-024-55928-3","article-title":"Deep learning solutions for smart city challenges in urban development","volume":"14","author":"Pengjun","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, H., and Zhong, M. (2020, January 14\u201316). A short-term traffic flow prediction model based on AutoEncoder and GRU. Proceedings of the 12th International Conference on Advanced Computational Intelligence, Dali, China.","DOI":"10.1109\/ICACI49185.2020.9177506"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2348\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:24:27Z","timestamp":1760106267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,7]]},"references-count":58,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072348"],"URL":"https:\/\/doi.org\/10.3390\/s24072348","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,7]]}}}