title: An algorithm for assessing the risk of traffic accident.
abstract: BACKGROUND
  This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area.
  
  
  METHODS
  The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm.
  
  
  RESULTS
  The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis.
  
  
  CONCLUSIONS
  The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.
authors.affiliation: Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. ceks.ng@polyu.edu.hk
authors.forename: Kwok-Suen
authors.initials: KS
authors.lastname: Ng
authors.forename: Wing-Tat
authors.initials: WT
authors.lastname: Hung
authors.forename: Wing-Gun
authors.initials: WG
authors.lastname: Wong
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D003625:Data Collection / Q000379:methods; D003663:Decision Trees; D040362:Geographic Information Systems / Q000592:standards*; D006723:Hong Kong; D006801:Humans; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods* / Q000592:standards; D012307:Risk Factors; D017751:Safety Management / Q000379:methods