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Bayesian non-parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia

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journal contribution
posted on 2022-10-21, 04:29 authored by JB Durand, F Forbes, Cong Duc Phan, Long TruongLong Truong, Hien NguyenHien Nguyen, F Dama
We develop a Bayesian non-parametric (BNP) model coupled with Markov random fields (MRFs) for risk mapping, to infer homogeneous spatial regions in terms of risks. In contrast to most existing methods, the proposed approach does not require an arbitrary commitment to a specified number of risk classes and determines their risk levels automatically. We consider settings in which the relevant information are counts and propose a so-called BNP hidden MRF (BNP-HMRF) model that is able to handle such data. The model inference is carried out using a variational Bayes expectation–maximisation algorithm and the approach is illustrated on traffic crash data in the state of Victoria, Australia. The obtained results corroborate well with the traffic safety literature. More generally, the model presented here for risk mapping offers an effective, convenient and fast way to conduct partition of spatially localised count data.

Funding

The authors are partly supported by the Inria project Lander.

History

Publication Date

2022-06-01

Journal

Australian and New Zealand Journal of Statistics

Volume

64

Issue

2

Pagination

34p. (p. 171-204)

Publisher

Wiley

ISSN

1369-1473

Rights Statement

© 2022 The Authors. Australian & New Zealand Journal of Statistics published by John Wiley & Sons Australia Ltd on behalf of Statistical Society of Australia. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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