  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.
