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Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression
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 to 80 mph on traffic safety. Full Bayesian multivariate Poisson lognormal regression models are applied to a data set 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 to 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.
Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression
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 to 80 mph on traffic safety. Full Bayesian multivariate Poisson lognormal regression models are applied to a data set 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 to 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.
Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression
Imprialou, Maria-Ioanna M. (author) / Quddus, Mohammed (author) / Pitfield, David E. (author)
Transportation Planning and Technology ; 39 ; 3-23
2016-01-02
21 pages
Article (Journal)
Electronic Resource
English
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