Employing Bayesian Networks and Conditional Probability Functions for Determining Dependences in Road Traffic Accidents Data

Kód výsledku v IS VaVaI

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Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?reload=true&punumber=7963867" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?reload=true&punumber=7963867</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Employing Bayesian Networks and Conditional Probability Functions for Determining Dependences in Road Traffic Accidents Data

Popis výsledku v původním jazyce

As we can all experience in our daily life, the traffic in the cities grows quickly, which, unfortunately, means also that the number of accidents grows, too. We try to find causes of accidents that happen for systematic reasons as we perceive eliminating such systematic errors as one of primary goals of smart cities idea. This paper deals with the accident data analysis using Bayesian Networks and conditional probability functions. We try to examine independence between variables in data sample in order to work with data of considerably large dimension. Our approach includes determining the structure of a Bayesian Network basing on a data sample and then utilizing computed probabilities in order to eliminate insignificant relations. We also use conditional probability functions to identify significant dependences basing only on data set. Finally we compare results obtained by both methods and use Goodman and Kruskal's lambda coefficient for confirming their accuracy.

Název v anglickém jazyce

Employing Bayesian Networks and Conditional Probability Functions for Determining Dependences in Road Traffic Accidents Data

Popis výsledku anglicky

As we can all experience in our daily life, the traffic in the cities grows quickly, which, unfortunately, means also that the number of accidents grows, too. We try to find causes of accidents that happen for systematic reasons as we perceive eliminating such systematic errors as one of primary goals of smart cities idea. This paper deals with the accident data analysis using Bayesian Networks and conditional probability functions. We try to examine independence between variables in data sample in order to work with data of considerably large dimension. Our approach includes determining the structure of a Bayesian Network basing on a data sample and then utilizing computed probabilities in order to eliminate insignificant relations. We also use conditional probability functions to identify significant dependences basing only on data set. Finally we compare results obtained by both methods and use Goodman and Kruskal's lambda coefficient for confirming their accuracy.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 Smart Cities Symposium Prague (SCSP) - IEEE PROCEEDINGS

ISBN

978-1-5386-3825-5

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

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Název nakladatele

IEEE Press

Místo vydání

New York

Místo konání akce

Prague

Datum konání akce

25. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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