Date of Publication :25th July 2024

Abstract:In the realm of transportation, where the dynamics of mobility intersect with considerations of safety, efficiency, and environmental impact, the study of driving behavior holds paramount importance. Within this domain, this project embarks on the exploration of novel methodologies to track driving behavior by integrating diverse smartphone sensors and employing sophisticated machine learning algorithms. The overarching problem addressed in this research pertains to the imperative need for effective solutions in understanding and managing driving behavior. Driving behaviors, ranging from aggressive maneuvers to fuel-efficient driving habits, significantly influence various facets of transportation systems, including road safety, fuel consumption, and emissions. Despite the awareness of these impacts, the challenge lies in developing methodologies that can accurately capture and analyze driving behaviors in a cost-effective manner, thereby paving the way for informed interventions. This project focuses on elucidating the optimal combination of smartphone sensors and machine learning techniques to discern driver aggression—a crucial aspect of driving behavior that directly correlates with road safety and overall driving experience. By leveraging a plethora of sensors available in Android smartphones, such as accelerometers, gyroscopes, and GPS, in conjunction with advanced classification algorithms, the research aims to gather comprehensive driving data and extract meaningful insights into driver behavior patterns. Through rigorous experimentation and analysis, the study endeavors to address the fundamental question of which sensor fusion and machine learning approach yield the most effective results in identifying and characterizing driver aggression. By doing so, it seeks to contribute to the development of cost-effective yet high-performance solutions for analyzing driving behavior, thereby laying the groundwork for interventions aimed at enhancing individual driving habits and, consequently, the broader transportation ecosystem.

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