Abstract: Driver behavior has recently emerged as a challenging topic in Traffic risk studies. Despite the advances in this topic, the challenges still remain. In fact, the current contribution deals with predicting at Real-time driver behavior based on machine learning techniques handling data sensing collected from smartphone sensors (accelerometer, gyroscope, GPS) and from OBD II. To ensure prediction at real time, we used a real-time architecture utilizing Atlas MongoDB service to synchronize data communication. Furthermore, we opt Random Forest model that demonstrates the highest performance compared to other models. This model has the advantage of predicting and preventing by warning a driver if his or her driving style is aggressive, moderate or slow. The proposed system aims to give more information about incidents to gain a better understanding of their causes.

Hajar LAZAR and Zahi JARIR, “Smart System for Driver Behavior Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151034

@article{LAZAR2024,
title = {Smart System for Driver Behavior Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151034},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151034},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Hajar LAZAR and Zahi JARIR}
}