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TRAFFIC ACCIDENT RISK CLASSIFICATION USING NEURAL NETWORKS

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F21%3A00353998" target="_blank" >RIV/68407700:21260/21:00353998 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.14311/nnw.2021.31.019" target="_blank" >https://doi.org/10.14311/nnw.2021.31.019</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/nnw.2021.31.019" target="_blank" >10.14311/nnw.2021.31.019</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    TRAFFIC ACCIDENT RISK CLASSIFICATION USING NEURAL NETWORKS

  • Original language description

    The article deals with the current issue of traffic accident risk classification in urban area. In connection with the increase in traffic in the Czech Republic, a higher probability of risks of traffic excesses can be expected in the future. If there is a traffic excess in the city, the aim is to propose a meaningful traffic management solution to minimize the social losses. The main needs are the early identification and classification of the cause of the traffic excess, finding a suitable alternative solution, quick application of that solution, and the rapid ability to resume operations in the area of congestion. Traffic prediction is one of the tools for the early identification of traffic excess. The article describes extensive research focused on the classification and prediction of the output variable of accident risk based on own programmed neural networks. The research outputs will be subsequently used for the creation of a traffic application for a selected urban area in the Czech Republic

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    21100 - Other engineering and technologies

Result continuities

  • Project

    <a href="/en/project/TJ01000183" target="_blank" >TJ01000183: Prediction of traffic excesses using neural networks</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

    2336-4335

  • Volume of the periodical

    31

  • Issue of the periodical within the volume

    05/21

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    11

  • Pages from-to

    343-353

  • UT code for WoS article

    000739166400003

  • EID of the result in the Scopus database

    2-s2.0-85123348014