Driver Scoring and Visualization

The University of Colorado Boulder's capstone project focused on analyzing telematics data to categorize driver profiles, develop a driver scoring system, and create an interactive dashboard for data visualization. This initiative aimed to derive insights from driver behavior, vehicle performance, and environmental interactions, providing valuable information to enhance driving standards and inform policy-making in vehicular management and urban planning.

The project began with the collection and preprocessing of a comprehensive telematics dataset. This data included metrics such as speed, acceleration, braking patterns, and environmental conditions. The team used this data to identify patterns and trends in driver behavior, which served as the foundation for developing a driver scoring system. This system assessed driving risks based on behavioral metrics, allowing for a more nuanced understanding of individual driving styles.

Using advanced machine learning techniques, the team categorized drivers into distinct profiles. These profiles were based on various driving behaviors, such as fuel efficiency, speed adherence, and safety compliance. The use of algorithms like XGBoost and Isolation Forest enabled the team to accurately group drivers and identify high-risk behaviors. This categorization was crucial for targeting specific interventions to improve driving habits and reduce accident rates.

A significant outcome of the project was the development of an interactive dashboard using Tableau. This dashboard provided real-time insights into vehicle and driver dynamics, displaying trends in fuel usage, speed, and safety compliance. Visualizations included average steering angles, seat belt usage, and the impact of vehicle features on driving behavior. The dashboard allowed users to easily interpret complex data, facilitating better decision-making in fleet management and urban planning.

The project concluded with several key insights and recommendations. The driver scoring system proved effective in identifying risky behaviors, while the interactive dashboard enhanced data accessibility and interpretation. The team suggested continuous monitoring and updates to the scoring system to adapt to changing driving patterns. Overall, the project demonstrated the potential of telematics data and advanced analytics to improve vehicular management and urban planning, promoting safer and more efficient transportation systems.