Commercial Vehicle Safety using Behavioral Measures and Machine Learning

This project works on ensuring road safety is of utmost significance within the transportation industry. While traditional static signage has its limitations, modern intelligent notification systems have emerged as a promising solution. These systems leverage a multitude of vehicle parameters to provide real-time and context-aware safety notifications to drivers. However, existing commercial solutions often lack the flexibility to deliver highly customized alerts for specific road segments, and these notifications are communicated with all drivers in a fleet. This research introduces a comprehensive curated notification system to address current limitations and shift from reactive to proactive safety. The study seeks to leverage a substantial industry dataset to enhance the accuracy of predictions in driving behavior analysis tasks using ensemble machine learning techniques, tailoring safety alerts for specific road segments. By coaching drivers with location-based, proactive safety alerts, it aims to enhance driver behavior and promote a safety culture in the transportation sector, ultimately saving lives, reducing costs, and fostering responsible driving practices.