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.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Trends and emerging research directions of sustainable aviation: A bibliometric analysis

    Yaşar Dinçer, Fatma Cande / Yirmibeşoğlu, Gözde / Bilişli, Yasemin et al. | Elsevier | 2024

    Free access

    Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions

    Yin, Xueyan / Wu, Genze / Wei, Jinze et al. | IEEE | 2022


    Traffic-accident studies

    Engineering Index Backfile | 1953


    Trends of traffic fatalities and DNA analysis in traffic accident investigation

    Takashi Taki / Mitsuyo Machida / Ryo Shimada | DOAJ | 2019

    Free access