Application of naturalistic driving data: A systematic review and bibliometric analysis

Alam, Md Rakibul; Batabyal, Debapreet; Yang, Kui; Brijs, Tom; Antoniou, Constantinos · 2023 · Crossref

DOI: 10.1016/j.aap.2023.107155

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Summary

This paper presents a systematic review and bibliometric analysis of the application of naturalistic driving data (NDD) in Intelligent Transportation Systems (ITS) research. The study addresses the lack of a comprehensive, multifaceted aggregation of NDD applications, noting that previous reviews were either limited to specific analysis techniques or failed to capture the evolutionary trends of the field over time. The authors aim to map the growth, conceptual structure, and most frequent application areas of NDD research to facilitate future studies. The methodology combines bibliometric analysis with a systematic literature review following the PRISMA 2020 framework. The authors searched the Web of Science database for publications between January 2002 and March 2022 using keywords such as “naturalistic driving data” and “naturalistic driving study data.” After filtering for journal articles and proceedings in English, and excluding reviews and meeting abstracts, a final dataset of 393 publications was analyzed. Bibliometric tools, including the Bibliometrix package in R, were used to assess research performance (publication counts, citations) and science mapping (keyword co-occurrence). Additionally, a data-driven approach clustered articles into specific ITS topics based on author keywords, removing highly overlapping categories to ensure distinct thematic analysis. The results indicate an annual growth rate of approximately 13.8% in NDD research, with exponential growth observed starting in 2013. *Accident Analysis & Prevention* was the most prolific publication source, followed by the *Transportation Research Record*. Citation analysis identified Dingus et al. (2016) as the most cited paper, with top influential authors including Guo F, Dingus TA, and Lee S. The conceptual structure of the field has shifted from early focuses on "active safety" to more recent emphases on "distraction," "driver behavior," and "traffic safety." The systematic review identified key application domains, with crash and crash surrogate analysis being the most prominent, followed by contextual driver behavior and advanced driver assistance systems (ADAS). Other significant topics included car-following behavior, intersection safety, age-specific driving behaviors, fuel efficiency, and autonomous vehicles. The study concludes that NDD has become a critical tool for assessing driving behavior and the impact of exogenous and endogenous factors on safety. By providing a detailed map of research trends and modeling objectives over a twenty-year period, the paper highlights the increasing complexity and density of NDD applications. It serves as a foundational resource for researchers seeking to understand the historical evolution of the field and identify prevalent modeling techniques, thereby guiding future investigations in ITS and driver safety.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• naturalistic crash near crash
• exposure measurement
• telematics crash prediction
• incidence prevalence
• induced exposure
• sex gender

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource