Abstract The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords “naturalistic driving data” and “naturalistic driving study data”. As a result, a set of 393 papers, Published between January 2002–March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.
Highlights A systematic review of the applications of naturalistic driving data (NDD) in ITS was conducted. A set of 393 papers, between 2002 and March 2022 was used to explore evolutionary trends in this field. A data-driven approach was taken to cluster similar research works together based on authors’ keywords. A set of bibliometric analysis techniques were used to find out conceptual structure, trends and impact of research works using NDD in the last two decades. Common application areas, modeling objectives, and analysis techniques utilizing NDD were thematically clustered.
Application of naturalistic driving data: A systematic review and bibliometric analysis
2023-06-04
Article (Journal)
Electronic Resource
English
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