Zobrazit minimální záznam

dc.contributor.authorBeshah, Tibebe
dc.contributor.authorEjigu, Dejene
dc.contributor.authorAbraham, Ajith
dc.contributor.authorSnášel. Václav
dc.contributor.authorKrömer, Pavel
dc.date.accessioned2012-09-07T11:23:27Z
dc.date.available2012-09-07T11:23:27Z
dc.date.issued2012
dc.identifier.citationNeural network world. 2012, vol. 22, issue 3, p. 215-244.cs
dc.identifier.issn1210-0552
dc.identifier.urihttp://hdl.handle.net/10084/94977
dc.description.abstractDescriptive analyses of the magnitude and situation of road safety in general and road accidents in particular is important, but understanding of data quality, factors related with dangerous situations and different interesting patterns in a data is of even greater importance. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role of road users on possible injury risks. The research employed TreeNet, Classification and Adaptive Regression Trees (CART), Random Forest (RF) and hybrid ensemble approach. 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 several analyses. Empirical results illustrate that data quality is a major problem that needs architectural guideline and the prototype models could classify accidents with promising accuracy. In addition an ensemble technique proves to be better in terms of predictive accuracy in the domain under study.cs
dc.language.isoencs
dc.publisherAkademie věd České republiky, Ústav informatiky a České vysoké učení technické v Praze, Fakulta dopravnícs
dc.relation.ispartofseriesNeural network worldcs
dc.relation.urihttp://isda2001.softcomputing.net/nnw2012_tibebe.pdfcs
dc.subjectroad safetycs
dc.subjectroad accidentcs
dc.subjectCARTcs
dc.subjectRandomForestcs
dc.subjectTreeNetcs
dc.subjectdata qualitycs
dc.titleKnowledge discovery from road traffic accident data in Ethiopia: Data quality, ensembling and trend analysis for improving road safetycs
dc.typearticlecs
dc.identifier.locationNení ve fondu ÚKcs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume22cs
dc.description.issue3cs
dc.description.lastpage244cs
dc.description.firstpage215cs
dc.identifier.wos000306821100001


Soubory tohoto záznamu

SouboryVelikostFormátZobrazit

K tomuto záznamu nejsou připojeny žádné soubory.

Tento záznam se objevuje v následujících kolekcích

Zobrazit minimální záznam