PMID- 23612560
OWN - NLM
STAT- MEDLINE
DCOM- 20131017
LR  - 20130507
IS  - 1879-2057 (Electronic)
IS  - 0001-4575 (Linking)
VI  - 56
DP  - 2013 Jul
TI  - Methodology for fitting and updating predictive accident models with trend.
PG  - 82-94
LID - S0001-4575(13)00101-2 [pii]
LID - 10.1016/j.aap.2013.03.009 [doi]
AB  - 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.
CI  - Copyright (c) 2013 Elsevier Ltd. All rights reserved.
FAU - Connors, Richard D
AU  - Connors RD
AD  - Institute for Transport Studies, University of Leeds, UK. 
      R.D.Connors@its.leeds.ac.uk
FAU - Maher, Mike
AU  - Maher M
FAU - Wood, Alan
AU  - Wood A
FAU - Mountain, Linda
AU  - Mountain L
FAU - Ropkins, Karl
AU  - Ropkins K
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130323
PL  - England
TA  - Accid Anal Prev
JT  - Accident; analysis and prevention
JID - 1254476
SB  - IM
MH  - Accidents, Traffic/*prevention & control/statistics & numerical data
MH  - Bayes Theorem
MH  - Binomial Distribution
MH  - England
MH  - Environment
MH  - *Models, Statistical
MH  - Poisson Distribution
MH  - Regression Analysis
MH  - Safety/*statistics & numerical data
MH  - Uncertainty
EDAT- 2013/04/25 06:00
MHDA- 2013/10/18 06:00
CRDT- 2013/04/25 06:00
PHST- 2012/07/04 00:00 [received]
PHST- 2013/01/08 00:00 [revised]
PHST- 2013/03/07 00:00 [accepted]
PHST- 2013/04/25 06:00 [entrez]
PHST- 2013/04/25 06:00 [pubmed]
PHST- 2013/10/18 06:00 [medline]
AID - S0001-4575(13)00101-2 [pii]
AID - 10.1016/j.aap.2013.03.009 [doi]
PST - ppublish
SO  - Accid Anal Prev. 2013 Jul;56:82-94. doi: 10.1016/j.aap.2013.03.009. Epub 2013 Mar 
      23.