Methodology for fitting and updating predictive accident models with trend [forthcoming]

TLDR: This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or byRe-fitting, including the choice of distributional assumption for overdispersion, and considerations about the most efficient and convenient ways to fit the required models.

Abstract: Reliable predictive accident models (PAMs) 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 aging 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 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, given the considerable advances that have been seen in statistical computing software in recent years