{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:00:33Z","timestamp":1776085233168,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Future Transportation"],"abstract":"<jats:p>The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety.<\/jats:p>","DOI":"10.3390\/futuretransp5040135","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T09:38:08Z","timestamp":1759397888000},"page":"135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models"],"prefix":"10.3390","volume":"5","author":[{"given":"Daniel","family":"Patr\u00edcio","sequence":"first","affiliation":[{"name":"School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-1384","authenticated-orcid":false,"given":"Paulo","family":"Loureiro","sequence":"additional","affiliation":[{"name":"Computer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-5745","authenticated-orcid":false,"given":"S\u00edlvio P.","family":"Mendes","sequence":"additional","affiliation":[{"name":"Computer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6561-5730","authenticated-orcid":false,"given":"Anabela","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Computer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4213-9302","authenticated-orcid":false,"given":"Rolando","family":"Miragaia","sequence":"additional","affiliation":[{"name":"Computer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"}]},{"given":"Iryna","family":"Husyeva","sequence":"additional","affiliation":[{"name":"Computer Science and Communication Research Centre, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","unstructured":"Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., Capstick, E., Reuel, A., Brynjolfsson, E., and Etchemendy, J. (2025). The AI Index 2025 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. Available online: https:\/\/aiindex.stanford.edu\/report\/."},{"key":"ref_2","unstructured":"P\u00daBLICO (2024, October 08). Portugal entre os seis pa\u00edses com mais mortes nas estradas entre 32 analisados. Available online: https:\/\/www.publico.pt\/2024\/06\/19\/sociedade\/noticia\/portugal-seis-paises-mortes-estradas-32-analisados-2094647."},{"key":"ref_3","unstructured":"(2025, May 14). Directorate-General for Mobility and Transport. EU Road Fatalities Drop by 3% in 2024, but Progress Remains Slow. European Commission. Available online: https:\/\/transport.ec.europa.eu\/news-events\/news\/eu-road-fatalities-drop-3-2024-progress-remains-slow-2025-03-18_en."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yu, J., Zhu, Y., Chen, Y., and Li, M. (2015, January 22\u201325). D3: Abnormal driving behaviors detection and identification using smartphone sensors. Proceedings of the IEEE SECON 2015, Seattle, WA, USA.","DOI":"10.1109\/SAHCN.2015.7338354"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dai, J., Teng, J., Bai, X., Shen, Z., and Xuan, D. (2010, January 22\u201325). Mobile phone based drunk driving detection. Proceedings of the 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Munich, Germany.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2010.8901"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TITS.2015.2462084","article-title":"Driver Behavior Analysis for Safe Driving: A Survey","volume":"16","author":"Kaplan","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.7307\/ptt.v29i2.2117","article-title":"Investigating car users\u2019 driving behaviour through speed analysis","volume":"29","author":"Eboli","year":"2017","journal-title":"Promet\u2014Traffic Transp."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ghandour, R., Potams, A.J., Boulkaibet, I., Neji, B., and Al Barakeh, Z. (2021). Driver Behavior Classification System Analysis Using Machine Learning Methods. Appl. Sci., 11.","DOI":"10.3390\/app112210562"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ben Brahim, S., Ghazzai, H., Besbes, H., and Massoud, Y. (June, January 27). A Machine Learning Smartphone-based Sensing for Driver Behavior Classification. Proceedings of the IEEE ISCAS 2022, Austin, TX, USA.","DOI":"10.1109\/ISCAS48785.2022.9937801"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ampountolas, A. (2024). Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons. J. Risk Financ. Manag., 17.","DOI":"10.3390\/jrfm17110475"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xie, Y., and Stravoravdis, S. (2023). Generating Occupancy Profiles for Building Simulations Using a Hybrid GNN and LSTM Framework. Energies, 16.","DOI":"10.3390\/en16124638"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aviles, M., Alvarez-Alvarado, J.M., Robles-Ocampo, J.B., Sevilla-Camacho, P.Y., and Rodr\u00edguez-Res\u00e9ndiz, J. (2024). Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization. Bioengineering, 11.","DOI":"10.3390\/bioengineering11010077"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cui, X., Chipusu, K., Ashraf, M.A., Riaz, M., Xiahou, J., and Huang, J. (2024). Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation. Symmetry, 16.","DOI":"10.3390\/sym16070920"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Ya, J., Xu, Z., Easa, S., Peng, K., Xing, Y., and Yang, R. (2023). Novel Neural-Network-Based Fuel Consumption Prediction Models Considering Vehicular Jerk. Electronics, 12.","DOI":"10.3390\/electronics12173638"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, H., Pan, Z., Fan, D., Zhou, C., and Wang, Z. (2022). Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction. Sensors, 22.","DOI":"10.3390\/s22155744"},{"key":"ref_18","unstructured":"Olah, C. (2025, March 15). Understanding LSTM Networks. Available online: http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","unstructured":"Joseph, V.R. (2025, January 15). Optimal Ratio for Data Splitting. Wiley Interdisciplinary Reviews: Computational Statistics. Available online: https:\/\/arxiv.org\/pdf\/2202.03326."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sivakumar, M., Vimal, S., Kalimuthu, M., Saravanan, V., Basha, M.S., Yenduri, A.K., and Jayagopal, P. (2024). Trade-off between training and testing ratio in machine learning models. PLoS ONE, 19, Available online: https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11419616\/.","DOI":"10.7717\/peerj-cs.2245"},{"key":"ref_22","first-page":"53","article-title":"An Efficient Deep Learning Model Based on Driver Behavior for Advanced Driver Assistance Systems","volume":"38","author":"Gheni","year":"2024","journal-title":"Rev. D\u2019intelligence Artif."},{"key":"ref_23","unstructured":"(2025, June 04). Results of Test Approaches. Available online: https:\/\/lstm-test-results.notion.site\/Results-of-Test-Approaches-1c27d118768a80b9bbfff8ec3a0d3062."}],"container-title":["Future Transportation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-7590\/5\/4\/135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T04:19:13Z","timestamp":1760069953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-7590\/5\/4\/135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"references-count":23,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["futuretransp5040135"],"URL":"https:\/\/doi.org\/10.3390\/futuretransp5040135","relation":{},"ISSN":["2673-7590"],"issn-type":[{"value":"2673-7590","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,2]]}}}