LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Elsevier
Languages: English
Types: Article
Subjects:
Reliable predictive accident models (PAMs) are essential to design and maintain safe road networks however, ongoing changes in road and vehicle design coupled with road safety initiatives, mean that these models can quickly become dated. Unfortunately, because the fitting of sophisticated PAMs including a wide range of explanatory variables is not a trivial task, available models tend to be based on data collected many years ago and seem unlikely to give reliable estimates of current accidents. Large, expensive studies to produce new models are likely to be, at best, only a temporary solution. This paper thus seeks to develop a practical and efficient methodology to allow currently available PAMs to be updated to give unbiased estimates of accident frequencies at any point in time. Two principal issues are examined: the extent to which the temporal transferability of predictive accident models varies with model complexity; and the practicality and efficiency of two alternative updating strategies. The models used to illustrate these issues are the suites of models developed for rural dual and single carriageway roads in the UK. These are widely used in several software packages in spite of being based on data collected during the 1980s. It was found that increased model complexity by no means ensures better temporal transferability and that calibration of the models using a scale factor can be a practical alternative to fitting new models.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Connors, R., Maher, M.J., Wood, A.G., Mountain, L.J., Ropkins, K., 2012. Methodology for fitting and updating predictive accident models with trend. Submitted at Accident Analysis and Prevention.
    • DfT, 2006. The COBA manual. Department for Transport, London.
    • DfT, 2010a. Road Casualties Great Britain: 2009. Department for Transport, London.
    • DfT, 2010b. Transport Trends 2009. Department for Transport, London.
    • Elvik, R., 2008. The predictive validity of empirical Bayes estimates of road safety. Accident Analysis & Prevention, 40, 1964-1969.
    • Elvik, R., 2010. The stability of long-term trends in the number of traffic fatalities in a sample of highly motorised countries. Accident Analysis & Prevention, 42, 245-260.
    • Hashim I.H., Bird R.N., 2005. Analysis of Accident Rates and Geometric Consistency Measures on Sections of Rural Single Carriageway. Road Safety on Four Continents (VTI/TRB Conference), Warsaw, Poland.
    • Lord, D., Mannering, F.L. (2010). The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A 44 (5), 291-305.
    • Maher, M.J., Summersgill, I., 1996. A comprehensive methodology for the fitting of predictive accident models. Accident Analysis and Prevention 28(3), 1996, 281-296
    • Maher, M.J., Mountain, L.J., 2009. The sensitivity of estimates of regression to the mean. Accident Analysis and Prevention 41, 861-868.
    • Maycock G., Hall, R.D., 1984. Crowthorne, UK.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article