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    Visualizing crash data patterns

    Visualizing crash data patterns

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    This paper demonstrates an approach that makes it easy to find patterns in traffic crash data-bases, and to specify their statistical significance. The detected patterns might help to prevent traffic crashes from happening, since they may be used to tailor campaigns to the community at hand. Unfortunately, the approach described here comes at a cost: it identifies a considerable amount of patterns, not all of them are being useful. The second disadvantage is that is needs a certain size of the data-base: here it has been applied to a data-base of the city of Berlin that contains about 1.6 Million (M) crashes from the years 2001 to 2016, of which about 0.9M had been used in the analysis.

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    Titel :

    Visualizing crash data patterns

    Beteiligte:
    Peter Wagner (Autor:in) / Ragna Hoffmann (Autor:in) / Marek Junghans (Autor:in) / Andreas Leich (Autor:in) / Hagen Saul (Autor:in)
    Erschienen in:

    Transactions on Transport Sciences, Vol 11, Iss 2, Pp 77-83 (2020)

    Erscheinungsdatum :

    2020

    ISSN :

    1802-9876

    DOI :

    https://doi.org/10.5507/tots.2020.008

    Medientyp :

    Aufsatz (Zeitschrift)

    Format :

    Elektronische Ressource

    Sprache :

    Unbekannt

    Schlagwörter :

    crash data patterns , crash analysis , contingency tables , pearson residual , cramers v , mosaic plot , Social sciences (General) , H1-99 , Transportation and communications , HE1-9990

    Metadata by DOAJ is licensed under ​CC BY-SA 1.0

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