title: Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model.
abstract: OBJECTIVE
  The severity of motorway accidents that occurred on the hard shoulder (HS) is higher than for the main carriageway (MC). This paper compares and contrasts the most important factors affecting the severity of HS and MC accidents on motorways in England.
  
  
  METHODS
  Using police reported accident data, the accidents that occurred on motorways in England are grouped into two categories (i.e., HS and MC) according to the location. A generalized ordered logistic regression model is then applied to identify the factors affecting the severity of HS and MC accidents on motorways. The factors examined include accident and vehicle characteristics, traffic and environment conditions, as well as other behavioral factors.
  
  
  RESULTS
  Results suggest that the factors positively affecting the severity include: number of vehicles involved in the accident, peak-hour traffic time, and low visibility. Differences between HS and MC accidents are identified, with the most important being the involvement of heavy goods vehicles (HGVs) and driver fatigue, which are found to be more crucial in increasing the severity of HS accidents.
  
  
  CONCLUSIONS
  Measures to increase awareness of HGV drivers regarding the risk of fatigue when driving on motorways, and especially the nearside lane, should be taken by the stakeholders.
authors.affiliation: School of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, United Kingdom. Electronic address: P.Michalaki@lboro.ac.uk.
authors.forename: Paraskevi
authors.initials: P
authors.lastname: Michalaki
authors.affiliation: School of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, United Kingdom. Electronic address: M.A.Quddus@lboro.ac.uk.
authors.forename: Mohammed A
authors.initials: MA
authors.lastname: Quddus
authors.affiliation: School of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, United Kingdom. Electronic address: D.E.Pitfield@lboro.ac.uk.
authors.forename: David
authors.initials: D
authors.lastname: Pitfield
authors.affiliation: Connect Roads - Balfour Beatty, Bramham Maintenance Compound, Spen Common Lane, Tadcaster LS24 9NS, United Kingdom. Electronic address: Andrew.Huetson@balfourbeatty.com.
authors.forename: Andrew
authors.initials: A
authors.lastname: Huetson
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data; D001336:Automobiles / Q000706:statistics & numerical data; D001364:Awareness; D004739:England / Q000453:epidemiology; D004777:Environment; D005221:Fatigue / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models
keywords: Accident severity; Fatigue; Generalized ordered logit model; Hard-shoulder; Motorway