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Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries

Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries
Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries

Several recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian multivariate model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting model collapses into a latent class multivariate model that is in the form of a flexible mixture of multivariate normal densities for which the number of mixtures (latent components) not only can be large but also can be inferred from the data as part of the analysis. Therefore, besides accounting for correlation among crash types through a heterogeneous correlation structure, the proposed model helps address unobserved heterogeneity through its latent class component. To our knowledge, this is the first study to propose and apply such a model in the transportation literature. We use the model to investigate the effects of various factors such as built environment characteristics on pedestrian and cyclist injury counts at signalized intersections in Montreal, modeling both outcomes simultaneously. We demonstrate that the homogeneity assumption of the standard multivariate model does not hold for the dataset used in this study. Consequently, we show how such a spurious assumption affects predictive performance of the model and the interpretation of the variables based on marginal effects. Our flexible model better captures the underlying complex structure of the correlated data, resulting in a more accurate model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation.

Correlated outcomes, Dependence structure, Latent class modeling, Mixture of multivariate normal densities, Multivariate modeling, Pedestrian/cyclist safety
2213-6657
16-27
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Joseph, Lawrence
495a60cb-4dff-4d23-b2d3-2ac9c0802dd2
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Joseph, Lawrence
495a60cb-4dff-4d23-b2d3-2ac9c0802dd2

Heydari, Shahram, Fu, Liping, Miranda-Moreno, Luis F. and Joseph, Lawrence (2017) Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries. Analytic Methods in Accident Research, 13, 16-27. (doi:10.1016/j.amar.2016.12.002).

Record type: Article

Abstract

Several recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian multivariate model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting model collapses into a latent class multivariate model that is in the form of a flexible mixture of multivariate normal densities for which the number of mixtures (latent components) not only can be large but also can be inferred from the data as part of the analysis. Therefore, besides accounting for correlation among crash types through a heterogeneous correlation structure, the proposed model helps address unobserved heterogeneity through its latent class component. To our knowledge, this is the first study to propose and apply such a model in the transportation literature. We use the model to investigate the effects of various factors such as built environment characteristics on pedestrian and cyclist injury counts at signalized intersections in Montreal, modeling both outcomes simultaneously. We demonstrate that the homogeneity assumption of the standard multivariate model does not hold for the dataset used in this study. Consequently, we show how such a spurious assumption affects predictive performance of the model and the interpretation of the variables based on marginal effects. Our flexible model better captures the underlying complex structure of the correlated data, resulting in a more accurate model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation.

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More information

Accepted/In Press date: 14 December 2016
e-pub ahead of print date: 24 December 2016
Published date: 1 March 2017
Keywords: Correlated outcomes, Dependence structure, Latent class modeling, Mixture of multivariate normal densities, Multivariate modeling, Pedestrian/cyclist safety

Identifiers

Local EPrints ID: 424172
URI: http://eprints.soton.ac.uk/id/eprint/424172
ISSN: 2213-6657
PURE UUID: 9cabd379-2a26-4cbf-94c8-b2ec2f13fcda

Catalogue record

Date deposited: 05 Oct 2018 11:31
Last modified: 07 Oct 2020 00:50

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Contributors

Author: Shahram Heydari
Author: Liping Fu
Author: Luis F. Miranda-Moreno
Author: Lawrence Joseph

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