Continuously updating predictive accident models using modern data sources

Lead Research Organisation: University of Liverpool
Department Name: School of Engineering


Reliable models to predict accident frequencies are essential to design and maintain safe road networks and yet the models in current use are based on data collected 20 or 30 years ago. Given that the national personal injury accident total fell by some 30% in the last 30 years, while over the same period road traffic almost doubled, significant errors in scheme appraisal and evaluation based on the models in current use seem inevitable. However, outdated predictive models tend to be applied within various computer programmes, without the user having a full appreciation of their limitations. The project will improve understanding of the limitations of currently available predictive accident models and how these can be overcome.The basic idea underlying this proposal is that, because modern databases now mean that access to accident, traffic and design data is much more straightforward, it is now possible to devise methods to update accident models to any point in time so that up-to-date predictions are always available. Up-to-date models would mean that the accidents associated with alternative design proposals could be more reliably predicted. It would also mean that EuroRAP type maps could be developed but showing true high risk locations (locations that have significantly more accidents than those predicted by the models given the nature of the site and the level of traffic flow) not just those that have potentially misleading high accident rates. Also safety improvement schemes implemented at high risk sites could be properly evaluated taking account of factors such as trend and regression-to-the-mean.In partnership with Lancashire County Council we will undertake a proof of concept project in which we will specifically test the fit of existing models and alternative updating strategies on the road network in Lancashire. Lancashire's award winning MARIO database will provide an ideal platform for this research. Three updating strategies have been identified as potentially feasible and further options may be identified in the course of the research. These will be applied and compared and the preferred approach identified. The principal outputs will be a tool to allow predictive models to be updated to any point in time and a workshop to provide guidance to practioners on both the data and data management systems needed for its application. The methodology developed will ensure that up-to-date models are always available for any location, permitting proper evaluation of safety impacts in the design and planning of road network changes.

Planned Impact

The main beneficiaries of this research will be national and local government practitioners and policy-makers, and ultimately the general public who will benefit from a safer road network. The British Government has recently set out an inspirational road safety strategy to make Britain's roads the safest in the world. British roads are already relatively safe by international standards but still over 2,500 people were killed on our roads in 2008. A key focus of Britain's new road safety strategy is to be that of improving the delivery of road safety through, in part, better use of data, more systematic information sharing, [and] better evaluation . This proposal will make a direct contribution to this, ensuring that reliable and up-to-date predictive accident models are always available for appraisal and evaluation. The proposed research will also be of direct benefit to local authorities charged with achieving the national casualty reduction targets beyond 2010. Predictive accident models play a vital role in the design, planning and evaluation of the road network. In design and planning, predictions of accident frequencies allow the safety implications of alternative options to be evaluated. Models can also be used to help identify sites which might benefit from treatment and to evaluate the effectiveness of any such treatments. The importance of predictive accident models makes it crucial that they are properly calibrated and transferable over time. This research will assess the impact of ageing on existing models, establish a methodology that will allow available models to be updated to any point in time and demonstrate the feasibility of the methodology in one county, Lancashire. Lancashire County Council will thus be an immediate beneficiary of the research. In addition, by means of a practitioners' workshop, guidance will be provided on both the data and data management systems needed to ensure that the methodology can be applied in any local authority area and can also respond to anticipated improvements in data availability. Thus, in the short to medium term, all local authorities will benefit from this research while, in the longer term, additional benefits will be achieved through the better use of new data sources. Ultimately the main beneficiaries will be the general public who will benefit from a safer road network. Road traffic accidents clearly have significant social impacts for the friends and relatives of those killed or injured but, perhaps less obviously, they also have significant economic impacts for the nation as a whole: the total value of prevention of all road accidents in Great Britain in 2008 is estimated to be 17.9bn. The proposed research has the potential to make a significant contribution to improved road safety, so benefiting the nation as a whole in financial terms as well as through improved health and enhanced quality of life.


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Connors RD (2013) Methodology for fitting and updating predictive accident models with trend. in Accident; analysis and prevention

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Wood A (2013) Updating predictive accident models of modern rural single carriageway A-roads in Transportation Planning and Technology

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Wood AG (2013) Updating outdated predictive accident models. in Accident; analysis and prevention