Assessing the Risk of Car Crashes in Road Networks

Borgoni, Riccardo; Gilardi, Andrea; Zappa, Diego

doi:10.1007/s11205-020-02295-x

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…A toy example representing the ideas behind the contraction of a road network is sketched in Figure 8. The red dots in Figure 8a represent redundant vertices since they can be removed without tampering the shape or the routability of the network 10 . The goal is to remove all redundant vertices and merge the corresponding edges, creating a graph which looks identical to the original one but with fewer edges.…”

Section: Sensitivity Analysis and The Modifiable Areal Unit Problemmentioning

confidence: 99%

“…The case study is the metropolitan area of Leeds (population 800000) in North England. We accessed Ordnance Survey data on major roads (3661 segments, total length 450 km), creating a spatial network substantially larger than previous studies, many of which report findings on only a few roads, with [10] representing a notable exception albeit with a simpler spatial structure. We present results for an entire metropolitan area, approximating more closely the level at which road policing activities and investment in road safety interventions are prioritised.…”

Section: Introductionmentioning

confidence: 99%

“…In case a network segment was longer than twice the threshold, we consider first order neighbours to avoid the creation of several islands (see Section 2). This problem is more pronounced for smaller values of the threshold 10. We say that the algorithm in[51] preserves the routability of a road network since it does not remove any vertex that could add a new component to the graph.…”

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confidence: 99%

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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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Section: Sensitivity Analysis and The Modifiable Areal Unit Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

Gilardi¹,

Mateu²,

Borgoni³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The case study is the metropolitan area of Leeds (population 800,000) in North England. We accessed Ordnance Survey data on major roads (3661 segments, total length 450 km), creating a spatial network substantially larger than previous studies, many of which report findings on only a few roads, with Borgoni et al (2020) representing a notable exception albeit with a simpler spatial structure. We present results for an entire metropolitan area, approximating more closely the level at which road policing activities and investments in road safety interventions are prioritised.…”

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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“…The first papers were developed using the areal approach [38,1,7,14], whereas the network lattice strategy gained popularity during the last years thanks to increasing computing capabilities and the rapid development of open-source spatial databases (such as Open Street Map) that provided the starting point for creating street networks at a wide range of spatial scales [6,5,36,13,15,26]. Both frameworks employ spatial smoothing techniques to simplify the estimation process, borrowing strength from neighbouring sites.…”

Section: Introductionmentioning

confidence: 99%

Measurement Error Models for Spatial Network Lattice Data: Analysis of Car Crashes in Leeds

Gilardi¹,

Borgoni²,

Presicce³

et al. 2022

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Road casualties represent an alarming concern for modern societies, especially in poor and developing countries. In the last years, several authors developed sophisticated statistical approaches to help local authorities implement new policies and mitigate the problem. These models are typically developed taking into account a set of socio-economic or demographic variables, such as population density and traffic volumes. However, they usually ignore that the external factors may be suffering from measurement errors, which can severely bias the statistical inference. This paper presents a Bayesian hierarchical model to analyse car crashes occurrences at the network lattice level taking into account measurement error in the spatial covariates.The suggested methodology is exemplified considering all road collisions in the road network of Leeds (UK) from 2011 to 2019. Traffic volumes are approximated at the street segment level using an extensive set of road counts obtained from mobile devices, and the estimates are corrected using a measurement error model. Our results show that omitting measurement error considerably worsens the model's fit and attenuates the effects of imprecise covariates.

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Assessing the Risk of Car Crashes in Road Networks

Cited by 7 publications

References 20 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

Measurement Error Models for Spatial Network Lattice Data: Analysis of Car Crashes in Leeds

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