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Future Transportation
Volume 5
Issue 4
10.3390/futuretransp5040135
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models

by
Daniel Patrício
1,
Paulo Loureiro
2,*,
Sílvio P. Mendes
2,
Anabela Bernardino
2,
Rolando Miragaia
2 and
Iryna Husyeva
3
1
School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
2
Computer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
3
Computer Science and Communication Research Centre, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine
*
Author to whom correspondence should be addressed.
Future Transp. 2025, 5(4), 135; https://doi.org/10.3390/futuretransp5040135
Submission received: 6 August 2025 / Revised: 24 September 2025 / Accepted: 27 September 2025 / Published: 2 October 2025
Versions Notes

Abstract

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.
Keywords: artificial intelligence; driver behavior; driving classification; smartphone sensors; LSTM; Conv1D; sliding windows; pattern-based driver aggressiveness behavior artificial intelligence; driver behavior; driving classification; smartphone sensors; LSTM; Conv1D; sliding windows; pattern-based driver aggressiveness behavior

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MDPI and ACS Style

Patrício, D.; Loureiro, P.; Mendes, S.P.; Bernardino, A.; Miragaia, R.; Husyeva, I. Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models. Future Transp. 2025, 5, 135. https://doi.org/10.3390/futuretransp5040135

AMA Style

Patrício D, Loureiro P, Mendes SP, Bernardino A, Miragaia R, Husyeva I. Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models. Future Transportation. 2025; 5(4):135. https://doi.org/10.3390/futuretransp5040135

Chicago/Turabian Style

Patrício, Daniel, Paulo Loureiro, Sílvio P. Mendes, Anabela Bernardino, Rolando Miragaia, and Iryna Husyeva. 2025. "Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models" Future Transportation 5, no. 4: 135. https://doi.org/10.3390/futuretransp5040135

APA Style

Patrício, D., Loureiro, P., Mendes, S. P., Bernardino, A., Miragaia, R., & Husyeva, I. (2025). Pattern-Based Driver Aggressiveness Behavior Assessment Using LSTM-Based Models. Future Transportation, 5(4), 135. https://doi.org/10.3390/futuretransp5040135

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