Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86084966" target="_blank" >RIV/61989100:27240/11:86084966 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/WICT.2011.6141426" target="_blank" >http://dx.doi.org/10.1109/WICT.2011.6141426</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WICT.2011.6141426" target="_blank" >10.1109/WICT.2011.6141426</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety

Popis výsledku v původním jazyce

This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Underthe umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.

Název v anglickém jazyce

Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety

Popis výsledku anglicky

This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Underthe umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2011 World Congress on Information and Communication Technologies, WICT 2011

ISBN

978-1-4673-0125-1

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

1241-1246

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Mumbai

Datum konání akce

11. 12. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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