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Title: Re-visiting crash-speed relationships: a new perspective in crash modelling
Authors: Imprialou, Marianna
Quddus, Mohammed A.
Pitfield, D.E.
Lord, Dominique
Keywords: Traffic speed
Crash frequency
Crash severity
Pre-crash conditions
Multivariate Poisson lognormal regression
Multivariate spatial correlation
Issue Date: 2016
Publisher: © The Authors. Published by Elsevier.
Citation: IMPRIALOU, M-I. ...et al., 2016. Re-visiting crash-speed relationships: new perspective in crash modelling. Accident Analysis and Prevention, 86, pp. 173-185.
Abstract: Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash-speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash-speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed-crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This research was partially funded by a grant from the UK Engineering and Physical Sciences Research Council (EPSRC) (Grant reference: EP/F018894/1).
Version: Published
DOI: 10.1016/j.aap.2015.10.001
URI: https://dspace.lboro.ac.uk/2134/19644
Publisher Link: http://dx.doi.org/10.1016/j.aap.2015.10.001
ISSN: 0001-4575
Appears in Collections:Published Articles (Architecture, Building and Civil Engineering)

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