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Accid Anal Prev. 1998 Sep;30(5):641-9.

The influence of trend on estimates of accidents at junctions.

Author information

1
Department of Civil Engineering, University of Liverpool, UK. l.mountain@liv.ac.uk

Abstract

While reliable estimates of expected accidents can be achieved by combining observed accidents and accident model predictions using an empirical Bayes approach, there are a number of obstacles to the widespread adoption of the method. This paper concentrates on problems associated with the available predictive models. Of particular concern is the effect on model predictions of accident trends over time resulting from, for instance, traffic growth or national road safety programmes. Since accident models invariably include traffic flow as an explanatory variable, the effects of flow changes can be included provided that account is taken of the nonlinear relationship between accidents and exposure. It is, however, common to assume that accident risk per unit of exposure is constant over time, whereas national data imply that accident risk is declining. In addition, there is a need, in practice, to rank and evaluate remedial sites in terms of the specific accident types or severities which might be targeted by treatment (for example, wet road accidents in the case of surface treatment). This then raises the question of whether the proportions of accidents of various types varies over time or with traffic flow and site characteristics. Generalized linear modelling was used to develop regression estimates of expected junction accidents (both in total and disaggregated by severity, road surface condition and lighting condition) which allow for the possibility of accident risk varying over time. Accident risk at the sample of some 500 junctions was shown to be declining annually by an average of 6%, with no significant difference in the value of trend between accident types. The factors which affected the proportions of accidents of various types included the method of junction control, speed limit and traffic flow.

PMID:
9678217
DOI:
10.1016/s0001-4575(98)00009-8
[Indexed for MEDLINE]

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