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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
31.10.2024
DOI
https://doi.org/10.7307/ptt.v36i5.576
ISSUE
2024 (Vol 36), Issue 5
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Copyright (c) 2024 Mesut ULU, Yusuf Sait TÜRKAN
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future Directions

Authors:Mesut ULU, Yusuf Sait TÜRKAN

Abstract

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

Keywords:traffic accident, prediction, bibliometrics analysis, research status, trend analysis, literature review

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