Pattern Mining of Older Drivers’ Driving Behavior Through Telematics-Data-Driven Unsupervised Learning

Increasing age and age-related health conditions can contribute to a higher risk of crash involvement and more severe physical injuries among older drivers. The current study uses in-vehicle sensing systems to analyze and cluster the overall driving behaviors of older drivers, precisely those aged 65 and older. The primary objective is to develop a framework that can help to observe patterns indicative of distinctive driving styles by clustering and interpreting similar patterns and leveraging self-organizing maps (SOMs) and deep embedded clustering (DEC) methods to reduce the complexity of the sensor data. The research uses two distinct groups of variables, including “speed,” “hard acceleration,” “hard braking” as constant variables, and “rpm,” “throttle positioning,” “fuel level,” “engine level,” and “ambient air temperature” as target variables from the in-vehicle sensor data in understanding driving behaviors and develops models in visualizing and interpreting complex driving patterns by classifying them. The results show that ${5} \times {5}$ grid SOMs have the ability to visualize multiple driving features concurrently, and DEC + K-means and DEC + agglomerative are the well-performed methods for determining the optimal number of clusters to analyze types of driving patterns. The clustering analysis identifies two distinct clusters as the optimal configuration, indicating that the predominant driving behavior among the targeted participants exhibits conservative patterns. The methodologies can be used for other driving features and demographics, making it relevant for broader applications in traffic analysis for understanding and visualizing driving patterns.