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 5,862 crashes of different
severities were recorded over an eight-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 northwest 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.Comment: 23 pages, 5 tables, 8 figure