Data Analytics in Monitoring Driver Behavior

Abstract

Driver behavior plays a critical role in road safety, vehicle efficiency, and traffic management. With the rise of connected vehicles and IoT devices, large volumes of real-time driving data are being collected. Data analytics is increasingly used to interpret this data, offering insights into driver habits, predicting risky behaviors, and improving vehicle performance. This paper explores the role of data analytics in monitoring driver behavior, including techniques like machine learning, sensor data fusion, and predictive modeling. Applications range from insurance telematics and fleet management to smart city planning. Ethical considerations and challenges such as data privacy and algorithmic bias are also discussed.

Keywords: Driver behavior, data analytics, telematics, machine learning, road safety, IoT, predictive modeling