Document Type

Student Coursework

Publication Date

3-2026

Abstract

Driver behavior analysis plays a central role in advancing road safety and enabling data-driven driver feedback. Although commercial telematics platforms offer sophisticated analytics, they are frequently expensive, proprietary, and optimized for enterprise-scale use. At the same time, low-cost On-Board Diagnostics II (OBD-II) adapters make telemetry collection widely accessible, but they typically do not provide higher-level behavioral interpretation.

In this paper, we present Driver Behavior Analyzer 2.0 (DBA 2.0), an offline-first, modular analytics framework that converts OBD-II and GPS telemetry into interpretable safety insights. DBA 2.0 supports ingestion of telemetry logs in CSV and JSON formats, data normalization, rule-based detection of unsafe driving events, geospatial alignment of detected events with GPS routes, and computation of a transparent distraction-oriented risk score. The framework further incorporates MongoDB-based persistence to enable longitudinal analysis and automated report generation. Rather than introducing a new predictive model, DBA 2.0 contributes a cohesive and extensible system architecture that bridges existing driver behavior analysis concepts with practical deployment. The proposed framework is well suited for educational settings, research applications, and small fleet analytics where transparency, affordability, and offline usability are important design requirements.

Program or Discipline Name

Computer and Information Sciences

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