Methodologies, principles and prospects of applying big data in safety science research

Abstract

It is clear that big data has numerous potential impacts in many fields. However, few papers discussed its applications in the field of safety science research. Additionally, there exist many problems that cannot be ignored when big data is applied to safety science, most outstanding of which is lack of universal supporting theory that guides how to apply big data to safety science research like methods, principles and approaches, etc. In other terms, it is not enough for big data to be viewed asa strong enabler for safety science applications mainly due to lack of universal and basic theory from the perspective of safety science. Considering the above analyzes, the two key objectives of this paper are: (1) to propose the connotation of safety big data (SBD) and its applying rules, methods and principles, and (2) to put forward some application prospects and challenges of big data to safety science research seen from theoretical research. First, by comparing SBD and traditional safety small data (SSD) from four aspects including theoretical research, typical research method, specific analysis method and processing mode, this paper puts forward the definition and connotation of SBD. Subsequently this paper further summarizes and extracts the application rules and methods of SBD. And then nine principles of SBD are explored and their relationship and application are addressed from the view of theory architecture and working framework in data processing flow. At last, this paper also discusses the potential applications and some hot issues of SBD. Overall, this paper will play an essential role in supporting the application of SBD. In addition, it will fill in the theory gaps in the field of SBD beyond traditional safety statistics, and further enriches the contents of safety science.