Traffic accidents—a modern epidemic

Mackay, Murray

doi:10.1080/00207237208709519

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Road casualties have been described as a global epidemic, representing the leading cause of death among young people worldwide (see, e.g. MacKay 1972; Nantulya & Reich, 2002). Car crashes and other types of collisions are responsible for more than 1 million deaths each year (1,250,000 in 2015, 17 deaths per 100,000 people), as reported by World Health Organization (2018).…”

Section: Introductionmentioning

confidence: 99%

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

Gilardi

Mateu

Borgoni

et al. 2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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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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Section: Introductionmentioning

confidence: 99%

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

Gilardi

Mateu

Borgoni

et al. 2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Road casualties have been described as a global epidemic [37,46], representing the leading cause of death among young people worldwide. Car crashes and other types of collisions are responsible for more than 1 million deaths each year (1250000 in 2015, 17 deaths per 100000 people) [68].…”

Section: Introductionmentioning

confidence: 99%

Multivariate hierarchical analysis of car crashes data considering a spatial network lattice

Gilardi¹,

Mateu²,

Borgoni³

et al. 2020

Preprint

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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 eight-year period (2011)(2012)(2013)(2014)(2015)(2016)(2017)(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 of the city. 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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Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction

Lin

Hsieh

2021

Proceedings of the 29th International Conference on Advances in Geographic Information Systems

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Traffic accidents—a modern epidemic

Cited by 3 publications

References 3 publications

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

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

Multivariate hierarchical analysis of car crashes data considering a spatial network lattice

Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction

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