Big Data Generated by Connected and Automated Vehicles for Safety Monitoring, Assessment and Improvement, Final Report (Year 3)

TL;DR

This report systematically analyzes how Big Data from connected and automated vehicles can enhance road safety by synthesizing research efforts, identifying trends, and proposing new analytical approaches.

Contribution

It introduces a comprehensive methodology for analyzing Big Data initiatives in CAV safety, including data collection, text analytics, and evolution modeling, to guide future safety improvements.

Findings

Identification of key trends in CAV Big Data research

Development of a taxonomy for safety-related data entities

Insights into the evolution of Big Data applications in CAV safety

Abstract

This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this domain, the report systematically compares Big Data initiatives related to transportation initiatives nationally and internationally and provides insights regarding the evolution of Big Data science applications related to CAVs and new challenges. The objectives addressed are: 1-Creating a database of Big Data efforts by acquiring reports, white papers, and journal publications; 2-Applying text analytics tools to extract key concepts, and spot patterns and trends in Big Data initiatives; 3-Understanding the evolution of CAV Big Data in the context of safety by quantifying granular taxonomies and modeling entity relations among contents in CAV Big Data research initiatives, and 4-Developing a foundation for exploring new approaches to tracking and analyzing CAV Big Data and related innovations. The study synthesizes and derives high-quality information from innovative research activities undertaken by various research entities through Big Data initiatives. The results can provide a conceptual foundation for developing new approaches for guiding and tracking the safety implications of Big Data and related innovations.