Multivariate hierarchical analysis of car crashes data considering a spatial network lattice
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Other documents of the author: Gilardi, Andrea; Mateu, Jorge; borgoni, riccardo; Lovelace, Robin
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Multivariate hierarchical analysis of car crashes data considering a spatial network latticeDate
2022-03-27Publisher
John Wiley & Sons, LtdISSN
0964-1998; 1467-985XBibliographic citation
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.12823Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
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Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 185, Iss. 3 (July 2022)Funder Name
DEMS Data Science Lab | Universita degli Studi di Milano-Bicocca
Project code
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.