title: Methodology for fitting and updating predictive accident models with trend.
abstract: Reliable predictive accident models (PAMs) (also referred to as Safety Performance Functions (SPFs)) have a variety of important uses in traffic safety research and practice. They are used to help identify sites in need of remedial treatment, in the design of transport schemes to assess safety implications, and to estimate the effectiveness of remedial treatments. The PAMs currently in use in the UK are now quite old; the data used in their development was gathered up to 30 years ago. Many changes have occurred over that period in road and vehicle design, in road safety campaigns and legislation, and the national accident rate has fallen substantially. It seems unlikely that these ageing models can be relied upon to provide accurate and reliable predictions of accident frequencies on the roads today. This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or by re-fitting. Models for accidents on rural single carriageway roads have been chosen to illustrate these issues, including the choice of distributional assumption for overdispersion, the choice of goodness of fit measures, questions of independence between observations in different years, and between links on the same scheme, the estimation of trends in the models, the uncertainty of predictions, as well as considerations about the most efficient and convenient ways to fit the required models.
authors.affiliation: Institute for Transport Studies, University of Leeds, UK. R.D.Connors@its.leeds.ac.uk
authors.forename: Richard D
authors.initials: RD
authors.lastname: Connors
authors.forename: Mike
authors.initials: M
authors.lastname: Maher
authors.forename: Alan
authors.initials: A
authors.lastname: Wood
authors.forename: Linda
authors.initials: L
authors.lastname: Mountain
authors.forename: Karl
authors.initials: K
authors.lastname: Ropkins
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D001499:Bayes Theorem; D016010:Binomial Distribution; D004739:England; D004777:Environment; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D012449:Safety / Q000706:statistics & numerical data*; D035501:Uncertainty