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    A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes

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    LI-THESIS.pdf (1.832Mb)
    Date
    2009-06-02
    Author
    Li, Linhua
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    Abstract
    This thesis develops a GIS-based Bayesian approach for area-wide traffic crash analysis. Five years of crash data from Houston, Texas, are analyzed using a geographic information system (GIS), and spatial-temporal patterns of relative crash risk are identified based on a hierarchical Bayesian approach. This Bayesian approach is used to filter the uncertainty in the data and identify and rank roadway segments with potentially high relative risks for crashes. The results provide a sound basis to take preventive actions to reduce the risks in these segments. To capture the real safety indications better, this thesis differentiates the risks in different directions of the roadways, disaggregates different road types, and utilizes GIS to analyze and visualize the spatial relative crash risks in 3-D views according to different temporal scales. Results demonstrate that the approach is effective in spatially smoothing the relative crash risks, eliminating the instability of estimates while maintaining real safety trends. The posterior risk maps show high-risk roadway segments in 3-D views, which is more reader friendly than the conventional 2-D views. The results are also useful for travelers to choose relatively safer routes.
    URI
    https://hdl.handle.net/1969.1/ETD-TAMU-1766
    Subject
    GIS
    Hierarchical Bayesian Modeling
    Crash Analysis
    3-D visualization
    Relative Risk
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    • Electronic Theses, Dissertations, and Records of Study (2002– )
    Citation
    Li, Linhua (2006). A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /ETD -TAMU -1766.

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