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