Abstract Recent studies have predicted that in 2030, traffic accidents will be the fifth leading cause of death worldwide. The costs of fatalities and driver injuries due to traffic accidents greatly affect the society. These insights call for investigating various aspects of traffic accident data analysis and modeling in numerous geographic regions (Sameen and Pradhan 2017a, b, c; Sameen et al. 2016). In particular, several researchers paid increasing attention to determining factors that greatly affect the severity of driver injuries caused by traffic accidents. Many approaches, such as logistic regression (LR) (Al-Ghamdi 2002), artificial neural networks (ANNs) (Delen et al. 2006; Moghaddam et al. 2011), support vector machines (SVMs) (Li et al. 2008, 2012), and Bayesian methods (Xie et al. 2009; de Oña et al. 2011), were explored to model traffic accident data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Modeling Traffic Accident Severity Using Neural Networks and Support Vector Machines


    Beteiligte:


    Erscheinungsdatum :

    2019-04-03


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Traffic accident severity prediction method based on recurrent neural network

    XU XUECAI / QIAN CHENG / XIAO DAIQUAN | Europäisches Patentamt | 2024

    Freier Zugriff

    Traffic accident severity assessment method based on single-vehicle traffic accident database

    NIU SHIFENG / CHANG DONGFENG / YU PENGCHENG et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Traffic forecasting using least squares support vector machines

    Zhang, Yang / Liu, Yuncai | Taylor & Francis Verlag | 2009


    Traffic sign array decomposition using support vector machines

    Gil-Jimenez, P. / Gomez-Moreno, H. / Siegmann, P. et al. | IEEE | 2008


    Traffic Sign Array Decomposition Using Support Vector Machines

    Jimenez, P.G. / Moreno, H.G. / Siegmann, P. et al. | British Library Conference Proceedings | 2008