HighlightsThe paper attempts to coalesce micro-level with macro-level data from existing geographic units for estimating crash models.A series of intersection crash models for four crash types with macro-level data for seven spatial units.Intersection models can be drastically improved by only including random-effects for macro-level entities.Intersection crash models are even further enhanced by including other macro-level variables.

    AbstractThere have been great efforts to develop traffic crash prediction models for various types of facilities. The crash models have played a key role to identify crash hotspots and evaluate safety countermeasures. In recent, many macro-level crash prediction models have been developed to incorporate highway safety considerations in the long-term transportation planning process. Although the numerous macro-level studies have found that a variety of demographic and socioeconomic zonal characteristics have substantial effects on traffic safety, few studies have attempted to coalesce micro-level with macro-level data from existing geographic units for estimating crash models. In this study, the authors have developed a series of intersection crash models for total, severe, pedestrian, and bicycle crashes with macro-level data for seven spatial units. The study revealed that the total, severe, and bicycle crash models with ZIP-code tabulation area data performs the best, and the pedestrian crash models with census tract-based data outperforms the competing models. Furthermore, it was uncovered that intersection crash models can be drastically improved by only including random-effects for macro-level entities. Besides, the intersection crash models are even further enhanced by including other macro-level variables. Lastly, the pedestrian and bicycle crash modeling results imply that several macro-level variables (e.g., population density, proportions of specific age group, commuters who walk, or commuters using bicycle, etc.) can be a good surrogate exposure for those crashes.


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    Titel :

    Intersection crash prediction modeling with macro-level data from various geographic units


    Beteiligte:
    Lee, Jaeyoung (Autor:in) / Abdel-Aty, Mohamed (Autor:in) / Cai, Qing (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2017-03-11


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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