Mining pattern from road accident data: Role of road user's behaviour and implications for improving road safety

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089242" target="_blank" >RIV/61989100:27240/13:86089242 - 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

Mining pattern from road accident data: Role of road user's behaviour and implications for improving road safety

Popis výsledku v původním jazyce

At the heart of any strategic effort to address a nationwide problem there is data or information. This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making senseout of it for improved decision making in the effort of reducing the problem of road safety. As part of an information architecture research for road safety data/information management 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), TreeNet and RandomForest approaches. To identify relevant patterns and illustrate theperformance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is used. After collecting the data and format it in the way suitable for the tool used model building and evaluation th

Název v anglickém jazyce

Mining pattern from road accident data: Role of road user's behaviour and implications for improving road safety

Popis výsledku anglicky

At the heart of any strategic effort to address a nationwide problem there is data or information. This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making senseout of it for improved decision making in the effort of reducing the problem of road safety. As part of an information architecture research for road safety data/information management 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), TreeNet and RandomForest approaches. To identify relevant patterns and illustrate theperformance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is used. After collecting the data and format it in the way suitable for the tool used model building and evaluation th

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

International Journal of Tomography and Statistics

ISSN

0972-9976

e-ISSN

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Svazek periodika

22

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IN - Indická republika

Počet stran výsledku

14

Strana od-do

73-86

Kód UT WoS článku

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EID výsledku v databázi Scopus

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