Multivariate Hierarchical Analysis of Car Crashes Data Considering a Spatial Network Lattice

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 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.

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes

“…Open ideas in the context of modeling crime data are many, but identifying crimes happening only in the network of streets in a city enhances the modeling task. In such a case, the Euclidean plane has to be substituted by the network support and this makes things different (see, for example, [ 37 ]). We can also think of models for location predictions of the following serial crime using the next hit predictor (NHP) method which adopts the framework of specific self-exciting processes created to characterize the correlations between crimes committed by the same criminal (see [ 38 ]).…”

Estructuras estocásticas notables en procesos puntuales espacio-temporales y medidas de riesgo bivariantes

“…• Identificar los delitos que ocurren solo en la red de calles de una ciudad, ya que mejora la tarea de modelización. En tal caso, el plano euclídeo tiene que ser sustituido por el soporte de la red (ver por ejemplo Gilardi et al, 2022).…”

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

“…Most importantly, micro-level aggregations provide the advantage of integrating roadway crash attributes and are preferred over macro-level aggregations ( 49 ). In addition, macro-level aggregation of traffic crash data is highly prone to cause the issue of modifiable area unit problems.…”