Multivariate hierarchical analysis of car crashesdata considering a spatial network lattice
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Title
Multivariate hierarchical analysis of car crashesdata considering a spatial network latticeDate
2022-03-27Publisher
Royal Statistical Society; WileyISSN
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), 1–28. Available from: https://doi.org/10.1111/rssa.12823Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
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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J R Stat Soc Series A. 2022;1–28.Related data
Additional supporting information may be found in the online version of the article at the publisher’s website.Funder Name
Universita degli Studi di Milano-Bicocca | CRUI-CARE
Project code
PID2019-107392RB-I00 and AICO/2019/198
Rights
© 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
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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