Smart application for traffic excess prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F20%3A00341858" target="_blank" >RIV/68407700:21260/20:00341858 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/SCSP49987.2020.9133935" target="_blank" >https://doi.org/10.1109/SCSP49987.2020.9133935</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SCSP49987.2020.9133935" target="_blank" >10.1109/SCSP49987.2020.9133935</a>

Result language
angličtina
Original language name
Smart application for traffic excess prediction
Original language description
The prediction of traffic excesses (traffic congestions and traffic accidents) is become a very important topic for many cities and regions. The number of cars in cities and the total traffic volumes in cities are increasing over time, and solutions will be needed to eliminate traffic accidents and prevent secondary excesses. This will ideally lead to time savings for transport users and, above all, to an increase in the safety, fluidity and environmental performance of the transport itself. The article deals with a research activity that aims to develop a separate module in the form of a traffic application that will be able to predict traffic excesses. The neural networks were the main tool for the development of traffic applications for prediction, namely multilayer neural networks with activation function sigmoidou. With regard to the focus of the conference Smart City, the article does not focus on extensive development and testing of neural network, but primarily on the description of the functionalities of the result, including a critical commentary on the problems of the current state of the application. The transport application is developed in collaboration with the scientific and commercial spheres and its future integration into the management platform for smart city management is expected.
Czech name
—
Czech description
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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)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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