Smart application for traffic excess prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Smart application for traffic excess prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Smart application for traffic excess prediction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

<a href="/cs/project/TJ01000183" target="_blank" >TJ01000183: Predikce dopravních excesů využívající neuronové sítě</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 Smart City Symposium Prague

ISBN

978-1-7281-6821-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE Press

Místo vydání

New York

Místo konání akce

Prague

Datum konání akce

25. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000590471100025