Information theoretical methods dedicated to accidents analysis for GIDAS database

  • Nowadays, traffic accidents are recorded in historical databases. Regarding the huge quantity of data, the use of data mining tools is essential to help Experts, for automatically extracting relevant information in order to establish and quantify relations between severity and potential factors of accidents. An innovative approach is here proposed for an in depth investigation of real world accidents data base. Mutual information ratio based on conditional entropies is used to quantity the association strength between an accident outcome descriptor (injury severity) and other potential association factors. Information theoretic methods help to select automatically groups of factors mostly responsible of the severity of accident.

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Metadaten
Author:Mathilde Mougeot, Robert Azencott
URN:urn:nbn:de:hbz:opus-bast-4806
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2012/08/02
Contributing corporation:Université Paris
Release Date:2012/08/02
Tag:Analyse; Datenbank; Datenerfassung; Konferenz; Risikobewertung; Statistik; Theorie; Unfall
Accident; Analyse (math); Conference; Data acquisition; Data bank; Risk assessment; Statistics; Theory
Comment:
weitere beteiligte Körperschaften: Université Denis Diderot; University of Houston
Source:3rd International Conference on ESAR "Expert Symposium on Accident Research", S.194-203
Institutes:Sonstige / Sonstige
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
collections:BASt-Beiträge / ITRD Sachgebiete / 81 Unfallstatistik
BASt-Beiträge / Tagungen / International Conference on ESAR / 3rd International Conference on ESAR

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