Multivariate hierarchical analysis of car crashes data considering a spatial network lattice
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Título
Multivariate hierarchical analysis of car crashes data considering a spatial network latticeFecha de publicación
2022-03-27Editor
John Wiley & Sons, LtdISSN
0964-1998; 1467-985XCita bibliográfica
Gilardi, A., Mateu, J., Borgoni, R. & Lovelace, R. (2022) Multivariate hierarchical analysis of car crashes data considering a spatial network lattice. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1150– 1177. Available from: https://doi.org/10.1111/rssa.12823Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a ... [+]
Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an 8-year period (2011–2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the north-west and south of city centre. We analyse the modifiable areal unit problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify ‘hotspots’ on the road network and to inform effective local interventions. [-]
Publicado en
Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 185, Iss. 3 (July 2022)Entidad financiadora
DEMS Data Science Lab | Universita degli Studi di Milano-Bicocca
Código del proyecto o subvención
PID2019-107392RB-I00 | AICO/2019/198
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info:eu-repo/semantics/openAccess
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© 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of
Royal Statistical Society.