Prediction of road accidents: A Bayesian hierarchical approach |
| |
| Authors: | Markus Deublein Matthias Schubert Bryan T Adey Jochen Köhler Michael H Faber |
| |
| Affiliation: | 1. Institute of Construction and Infrastructure Management, Swiss Federal Institute of Technology ETH, Zurich, Switzerland;2. Matrisk GmbH, Managing Technical Risks, Zurich, Switzerland;3. Department of Structural Engineering, Norwegian University of Science and Technology NTNU, Trondheim, Norway;4. Department of Civil Engineering, Technical University of Denmark, DTU, Kgs. Lyngby, Denmark |
| |
| Abstract: | In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. |
| |
| Keywords: | Road safety assessment Accident prediction Injury accidents Bayesian Probabilistic Networks Accident risk modelling Multivariate regression analysis Hierarchical Bayes |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
