Assessment of the Impact of Traffic Police Preventive Interventions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F17%3A00004208" target="_blank" >RIV/46747885:24310/17:00004208 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Assessment of the Impact of Traffic Police Preventive Interventions

Popis výsledku v původním jazyce

Free publicly available datasets describing weather and traffic accidents in the Czech Republic have been used for the training of a feed-forward neural network so that it could predict the level of the number of traffic accidents and their cost from weather, weekday, and month. The neural network learns the data for each of the 14 Czech regions separately and also the idea of cross-validation is utilized. The data for each learning task have been separated into Training Set, Development Test Set, and Test Set. Then a statistically significant number of experiments with neural network to get the accuracy of the prediction of the Test Set that happens when the accuracy of the Development Test Set is maximized have been conducted. The aim of the research is to learn whether this methodology can statistically detect any significant difference of the accuracy of prediction between the Test Set formed from days with interventions reported by the Czech Police and the randomly selected Test Set using the assumption that the neural network learns dependencies not affected by preventive interventions.

Název v anglickém jazyce

Assessment of the Impact of Traffic Police Preventive Interventions

Popis výsledku anglicky

Free publicly available datasets describing weather and traffic accidents in the Czech Republic have been used for the training of a feed-forward neural network so that it could predict the level of the number of traffic accidents and their cost from weather, weekday, and month. The neural network learns the data for each of the 14 Czech regions separately and also the idea of cross-validation is utilized. The data for each learning task have been separated into Training Set, Development Test Set, and Test Set. Then a statistically significant number of experiments with neural network to get the accuracy of the prediction of the Test Set that happens when the accuracy of the Development Test Set is maximized have been conducted. The aim of the research is to learn whether this methodology can statistically detect any significant difference of the accuracy of prediction between the Test Set formed from days with interventions reported by the Czech Police and the randomly selected Test Set using the assumption that the neural network learns dependencies not affected by preventive interventions.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mathematical Methods in Economics MME 2017

ISBN

978-80-7435-678-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

505-510

Název nakladatele

Gaudeamus, University of Hradec Králové

Místo vydání

Hradec Králové

Místo konání akce

Hradec Králové

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000427151400086