{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:23:58Z","timestamp":1777501438202,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate and timely traffic information is a vital element in intelligent transportation systems and urban management, which is vitally important for road users and government agencies. However, existing traffic prediction approaches are primarily based on standard machine learning which requires sharing direct raw information to the global server for model training. Further, user information may contain sensitive personal information, and sharing of direct raw data may lead to leakage of user private data and risks of exposure. In the face of the above challenges, in this work, we introduce a new hybrid framework that leverages Federated Learning with Local Differential Privacy to share model updates rather than directly sharing raw data among users. Our FL-LDP approach is designed to coordinate users to train the model collaboratively without compromising data privacy. We evaluate our scheme using a real-world public dataset and we implement different deep neural networks. We perform a comprehensive evaluation of our approach with state-of-the-art models. The prediction results of the experiment confirm that the proposed scheme is capable of building performance accurate traffic predictions, improving privacy preservation, and preventing data recovery attacks.<\/jats:p>","DOI":"10.3390\/info13080381","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T02:42:53Z","timestamp":1660099373000},"page":"381","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3438-3885","authenticated-orcid":false,"given":"Mohammed","family":"Akallouch","sequence":"first","affiliation":[{"name":"Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez 30050, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oussama","family":"Akallouch","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez 30050, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid","family":"Fardousse","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez 30050, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afaf","family":"Bouhoute","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez 30050, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ismail","family":"Berrada","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Mohammed VI Polytechnic University, Benguerir 43150, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"Lv","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aung, N., Zhang, W., Dhelim, S., and Ai, Y. (2020). T-Coin: Dynamic traffic congestion pricing system for the Internet of Vehicles in smart cities. Information, 11.","DOI":"10.3390\/info11030149"},{"key":"ref_3","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., and Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv."},{"key":"ref_4","unstructured":"Aono, Y., Hayashi, T., Wang, L., and Moriai, S. (2017, January 6\u20137). Privacy-preserving deep learning: Revisited and enhanced. Proceedings of the International Conference on Applications and Techniques in Information Security, Auckland, New Zealand."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TIFS.2017.2787987","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Phong","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bindschaedler, V., Shokri, R., and Gunter, C.A. (2017). Plausible deniability for privacy-preserving data synthesis. arXiv.","DOI":"10.14778\/3055540.3055542"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ojeda, L.L., Kibangou, A.Y., and De Wit, C.C. (2013, January 17\u201319). Adaptive Kalman filtering for multi-step ahead traffic flow prediction. Proceedings of the 2013 IEEE American Control Conference, Washington, DC, USA.","DOI":"10.1109\/ACC.2013.6580568"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.proeng.2017.04.417","article-title":"Traffic flow prediction using Kalman filtering technique","volume":"187","author":"Kumar","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TITS.2009.2021448","article-title":"Multivariate short-term traffic flow forecasting using time-series analysis","volume":"10","author":"Ghosh","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Olayode, I.O., Tartibu, L.K., Okwu, M.O., and Ukaegbu, U.F. (2021). Development of a hybrid artificial neural network-particle swarm optimization model for the modelling of traffic flow of vehicles at signalized road intersections. Appl. Sci., 11.","DOI":"10.3390\/app11188387"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Tan, H., Peter, J., Shen, B., and Ran, B. (2015, January 24\u201327). Short-term traffic flow prediction based on multilinear analysis and k-nearest neighbor regression. Proceedings of the COTA International Conference of Transportation Professionals (CICTP), Beijing, China.","DOI":"10.1061\/9780784479292.051"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"397","DOI":"10.3846\/16484142.2013.818057","article-title":"Short term traffic flow prediction in heterogeneous condition using artificial neural network","volume":"30","author":"Kumar","year":"2015","journal-title":"Transport"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.1109\/TNNLS.2016.2574840","article-title":"Optimized structure of the traffic flow forecasting model with a deep learning approach","volume":"28","author":"Yang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","article-title":"Deep learning for short-term traffic flow prediction","volume":"79","author":"Polson","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","article-title":"A hybrid deep learning based traffic flow prediction method and its understanding","volume":"90","author":"Wu","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fredianelli, L., Carpita, S., Bernardini, M., Del Pizzo, L.G., Brocchi, F., Bianco, F., and Licitra, G. (2022). Traffic flow detection using camera images and machine learning methods in ITS for noise map and action plan optimization. Sensors, 22.","DOI":"10.3390\/s22051929"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ul Abideen, Z., Sun, H., Yang, Z., and Ali, A. (2020). The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow. Appl. Sci., 10.","DOI":"10.3390\/app10217778"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Pan, L. (2015, January 19\u201321). Predicting short-term traffic flow by long short-term memory recurrent neural network. Proceedings of the 2015 IEEE international conference on smart city\/SocialCom\/SustainCom (SmartCity), Chengdu, China.","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., and Li, L. (2016, January 11\u201313). Using LSTM and GRU neural network methods for traffic flow prediction. Proceedings of the 2016 IEEE 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiao, Y., and Yin, Y. (2019). Hybrid LSTM neural network for short-term traffic flow prediction. Information, 10.","DOI":"10.3390\/info10030105"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karimzadeh, M., Schwegler, S.M., Zhao, Z., Braun, T., and Sargento, S. (July, January 28). MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting. Proceedings of the 2021 IEEE International Wireless Communications and Mobile Computing (IWCMC), Harbin, China.","DOI":"10.1109\/IWCMC51323.2021.9498905"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karpathy, A., and Fei-Fei, L. (2015, January 7\u201312). Deep visual-semantic alignments for generating image descriptions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ren, J., Hu, Y., Tai, Y.W., Wang, C., Xu, L., Sun, W., and Yan, Q. (2016, January 12\u201317). Look, listen and learn\u2014A multimodal LSTM for speaker identification. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10471"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, R., Fung, B., and Desai, B.C. (2011). Differentially private trajectory data publication. arXiv.","DOI":"10.1145\/2339530.2339564"},{"key":"ref_27","unstructured":"Hoh, B., and Gruteser, M. (2005, January 5\u20139). Protecting location privacy through path confusion. Proceedings of the IEEE First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM\u201905), Washington, DC, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rass, S., Fuchs, S., Schaffer, M., and Kyamakya, K. (2008, January 15). How to protect privacy in floating car data systems. Proceedings of the Fifth ACM International Workshop on VehiculAr Inter-NETworking, San Francisco, CA, USA.","DOI":"10.1145\/1410043.1410047"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hoh, B., Gruteser, M., Herring, R., Ban, J., Work, D., Herrera, J.C., Bayen, A.M., Annavaram, M., and Jacobson, Q. (2008, January 17\u201320). Virtual trip lines for distributed privacy-preserving traffic monitoring. Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, Breckenridge, CO, USA.","DOI":"10.1145\/1378600.1378604"},{"key":"ref_30","unstructured":"Lu, S., Yao, Y., and Shi, W. (2019, January 9). Collaborative learning on the edges: A case study on connected vehicles. Proceedings of the 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19), Renton, WA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1049\/trit.2019.0049","article-title":"Federated learning framework for mobile edge computing networks","volume":"5","author":"Fantacci","year":"2020","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saputra, Y.M., Hoang, D.T., Nguyen, D.N., Dutkiewicz, E., Mueck, M.D., and Srikanteswara, S. (2019, January 9\u201313). Energy demand prediction with federated learning for electric vehicle networks. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Big Island, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8464","DOI":"10.1109\/TII.2021.3055283","article-title":"FASTGNN: A topological information protected federated learning approach for traffic speed forecasting","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6532","DOI":"10.1109\/TII.2019.2945367","article-title":"Efficient and privacy-enhanced federated learning for industrial artificial intelligence","volume":"16","author":"Hao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","unstructured":"McMahan, H.B., Ramage, D., Talwar, K., and Zhang, L. (2017). Learning differentially private recurrent language models. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y. (2019, January 15). A hybrid approach to privacy-preserving federated learning. Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, London, UK.","DOI":"10.1145\/3338501.3357370"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","article-title":"Personalized federated learning with differential privacy","volume":"7","author":"Hu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Triastcyn, A., and Faltings, B. (2019, January 9\u201312). Federated learning with bayesian differential privacy. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","article-title":"Privacy-preserving traffic flow prediction: A federated learning approach","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zeng, T., Guo, J., Kim, K.J., Parsons, K., Orlik, P., Di Cairano, S., and Saad, W. (2021, January 11\u201315). Multi-task federated learning for traffic prediction and its application to route planning. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575211"},{"key":"ref_42","unstructured":"Chen, J., Pan, X., Monga, R., Bengio, S., and Jozefowicz, R. (2016). Revisiting distributed synchronous SGD. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/TMC.2012.208","article-title":"Local differential perturbations: Location privacy under approximate knowledge attackers","volume":"12","author":"Dewri","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lyu, L., Yu, H., and Yang, Q. (2020). Threats to federated learning: A survey. arXiv.","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref_46","unstructured":"Chen, C. (2003). Freeway Performance Measurement System (PeMS), University of California."},{"key":"ref_47","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_48","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","article-title":"Support vector machines for wind speed prediction","volume":"29","author":"Mohandes","year":"2004","journal-title":"Renew. Energy"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2078-2489\/13\/8\/381\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:13Z","timestamp":1760141173000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2078-2489\/13\/8\/381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["info13080381"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/info13080381","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,9]]}}}