Analysis of residence characteristics of at-fault drivers in traffic crashes

    Authors

    J. Lee; M. Abdel-Aty;K. Choi

    Comments

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    Abbreviated Journal Title

    Saf. Sci.

    Keywords

    Bayesian modeling; Poisson-lognormal model; Residence analysis; ZIP; code; Socioeconomic factors; Census data; MOTOR-VEHICLE CRASHES; SPATIAL-ANALYSIS; MODELS; FREQUENCY; POPULATION; DEPRESSION; PREDICTION; MORTALITY; SEVERITY; CAMPAIGN; Engineering, Industrial; Operations Research & Management Science

    Abstract

    In recent years many studies have investigated traffic crashes with various contributing factors at the macroscopic level. Nevertheless, while previous studies have concentrated only on zones where the crash occurred, there have been few studies that focused on residence characteristics associated with the origin of the drivers causing traffic crashes, so called at-fault drivers. Intuitively, it is reasonable to assume that the number of at-fault drivers is related to socio-demographic features of the at-fault drivers' residence area. Thus, the main objective of this study is to find out the relationship between the number of at-fault drivers and zonal characteristics of the residence where at-fault drivers came from. The Bayesian Poisson-lognormal model was adopted to find out the contributing factors of the residence zones on the number of crashes based on the at-fault drivers. The findings from the study implied that the crash occurrence is not only affected by roadway/traffic factors but also by several demographic and socioeconomic characteristics of residence zones. The result from this study can be used to identify zones with a higher potential of at-fault drivers; thus we can concentrate on these zones for safety treatments, including more targeted awareness, education or stricter enforcement. (C) 2014 Elsevier Ltd. All rights reserved.

    Journal Title

    Safety Science

    Volume

    68

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    6

    Last Page

    13

    WOS Identifier

    WOS:000338402100002

    ISSN

    0925-7535

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