DriveC: Web Application for Classification of Driving Events
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Abstract
This paper presents a web application designed to analyze and classify driving behaviors using data from gyroscope and accelerometer sensors embedded in smartphones. By harnessing real-time sensor data, the tool accurately calculates driving risk, enabling continuous and comprehensive driver behavior assessment. An advanced Long Short Term Memory neural network model was implemented, chosen for its superior capability to capture temporal dependencies in sequential data and effectively identify complex driving patterns. The model achieved a notable accuracy of 86.36 percent, underscoring its reliability and strong potential for real-time deployment. This innovative approach provides a practical and precise method for driving risk assessment, with significant implications for enhancing safety in the insurance industry and road management systems.
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