Loughborough University Institutional Repository Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model This item was submitted to Loughborough University's Institutional Repository by the/an author. Citation: WANG, C., QUDDUS, M.A. and ISON, S.G., Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model. Accident Analysis and Prevention, 43(6), pp.1979-1990 Metadata Record: https://dspace.lboro.ac.uk/2134/8702 Version: Accepted for publication Publisher: c Elsevier Please cite the published version. This item was submitted to Loughborough’s Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions. For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ 1 Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model Chao Wang*, Mohammed A Quddus, Stephen G Ison Transport Studies Group Department of Civil and Building Engineering Loughborough University Loughborough, Leicestershire LE11 3TU United Kingdom * Corresponding author – Tel: +44 (0)1509 564682; Fax: +44 (0)1509 223981. Email addresses:
[email protected] (Chao Wang);
[email protected] (Mohammed A Quddus);
[email protected] (Stephen G Ison). Abstract Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g.