Crash Prediction Models and Methodological Issues

Authors

  • Anteneh Afework Mekonnen ORCID
    Affiliation

    Department of Transport Technology and Economics, Faculty of Transportation and Vehicle Engineering, Budapest University of Technology and Economics, H-1521 Budapest, P.O.B. 91, Hungary

  • Tibor Sipos ORCID
    Affiliation

    Department of Transport Technology and Economics, Faculty of Transportation and Vehicle Engineering, Budapest University of Technology and Economics, H-1521 Budapest, P.O.B. 91, Hungary

https://doi.org/10.3311/PPtr.16295

Abstract

The conducted literature review aimed to provide an overall perspective on the significant findings of past research works related to vehicle crashes and prediction models. The literature review also provided information concerning past road safety research methodology and viable statistical analysis and computing tools. Though the selection of a specific model hinges on the objective of the research and nature of the response, when compared to statistical modeling techniques, Artificial Neural Networks (ANNs), which can model complex nonlinear relationships among dependent and independent parameters, have been witnessed to be very powerful.

Keywords:

crash prediction models, Artificial Neural Network, statistical methods, road safety, soft computing tools, methodological issues

Published Online

2022-05-09

How to Cite

Mekonnen, A. A., Sipos, T. (2022) “Crash Prediction Models and Methodological Issues”, Periodica Polytechnica Transportation Engineering, 50(3), pp. 267–272. https://doi.org/10.3311/PPtr.16295

Issue

Section

Articles