Highlight The spatial–temporal drink-driving pattern was analyzed based on the random breath test data. Clusters of drink-driving patterns were set out with respect to geographical area and time period. Drivers in rural areas tend to consume more alcohol than those in urban areas. Seasonal variation and vehicle class were found to determine the breath alcohol concentration levels among drivers.

    Abstract Normally, bars and restaurants are the preferred locations for drinking. Therefore, there is concern that the roads in bar and restaurant areas could have a higher probability of drink-drivers and alcohol-related road crashes. Many studies have been conducted to model the association between drinking locations and the prevalence of drink-driving, so that cost-effective enforcement strategies can be developed to combat drink-driving. In this study, a cluster analysis approach was applied to model the spatial–temporal variation of drink-driving distribution in Hong Kong. Six spatial–temporal clusters of drink-driving distribution emerged from the data: (i) bar and restaurant area, weekend-overnight; (ii) bar and restaurant area, other timespan; (iii) urban area, weekend-overnight; (iv) urban area, other timespans; (v) rural area, weekend-overnight; and (vi) rural area, other timespans. Next, separate zero-inflated regression models were established to identify the factors contributing to the prevalence of drink-driving for each of the six recognized clusters. The results indicated that drivers in rural areas tend to consume more alcohol than those in urban areas, regardless of the time period. In addition, both seasonal variation and vehicle class were found to determine the breath alcohol concentration (BrAC) levels among drivers.


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    Title :

    Spatial–temporal analysis of drink-driving patterns in Hong Kong


    Contributors:
    Li, Y.C. (author) / Sze, N.N. (author) / Wong, S.C. (author)

    Published in:

    Publication date :

    2013-06-26


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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    Li, Y.C. / Sze, N.N. / Wong, S.C. | Taylor & Francis Verlag | 2014



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