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Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression

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journal contribution
posted on 26.11.2015 by Marianna Imprialou, Mohammed Quddus, D.E. Pitfield
Speed limit changes are considered to lead to proportional changes in the number and severity of crashes. To predict the impact of a speed limit alteration, it is necessary to define a relationship between crashes and speed on a road network. This paper examines the relationship of crashes with speed, as well as with other traffic and geometric variables, on the UK motorways in order to estimate the impact of a potential speed limit increase from 70 mph to 80 mph on traffic safety. Full Bayesian multivariate Poisson lognormal regression models are applied to a dataset aggregated using the condition-based approach for crashes by vehicle (i.e. single-vehicle and multiple-vehicle) and severity (i.e. fatal or serious and slight). The results show that single-vehicle crashes of all severities and fatal or serious injury crashes involving multiple vehicles increase at higher speed conditions and particularly when these are combined with lower volumes. Slight injury multiple-vehicle crashes are found not to be related with high speeds, but instead with congested traffic. Using the speed elasticity values derived from the models the predicted annual increase in crashes after a speed limit increase on the UK motorway is found to be 6.2-12.1 % for fatal or serious injury crashes and 1.3-2.7% for slight injury, or else up to 167 more crashes.

History

School

  • Architecture, Building and Civil Engineering

Published in

Transportation Planning and Technology

Citation

IMPRIALOU, M-I., QUDDUS, M.A. and PITFIELD, D.E., 2016. Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression. Transportation Planning and Technology, 39 (1), pp. 3-23.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Transportation Planning and Technology on 25th November 2015, available online: http://www.tandfonline.com/10.1080/03081060.2015.1108080.

ISSN

1029-0354

Language

en

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