Bayesian hierarchical models for linear networks
- PMID: 35707112
- PMCID: PMC9042008
- DOI: 10.1080/02664763.2020.1864814
Bayesian hierarchical models for linear networks
Abstract
The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.
Keywords: Bayesian methods; Hierarchical models; linear networks; point processes.
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Conflict of interest statement
No potential conflict of interest was reported by the author(s).
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