Purpose: Over the past 10 years, building on road infrastructure data, crash prediction models (CPMs) have become fundamental scientific tools for road safety management. However, there is a gap between state-of-the-art and state-of-the-practice, with the practical application lagging behind scientific progress. This motivated a review of international experience with CPMs from perspectives of application by practitioners and development by researchers. The objective of the paper is to improve practitioner understanding of modelling road safety performance using CPMs for crash frequency estimation, leading to their greater uptake in improving road safety. In short, why and how should road safety practitioners consider CPMs? Methods: Both scientific and practice-oriented literature was retrieved, using academic sources, as well as reports of road agencies or institutes. The selection was limited to English language. Results: From the review it is clear that developing CPMs is not a straightforward task: there are many available choices and decisions to be made during the process without definite guidance. This explains the diversity of approaches, techniques, and model types. The paper explains how some fundamental modelling decisions affect practical aspects of modelling safety performance. Conclusions: There is a need to identify CPM solutions that will be scientifically sound and feasible in practitioners' context. Together with increased communication between researchers and practitioners, these solutions will help overcome the identified challenges and increase use of CPMs.

No abstract

No abstract

No abstract

This paper focuses on application of crash prediction models in network screening. The two main questions were ( a) What variables should be involved in the model? and ( b) What length should the modeled period be? Answers to these questions should provide guidelines for developing an updatable crash prediction model (i.e., a model that is both reliable and simple so that its updating for periodical network screening is not highly demanding). Data on approximately 1,000 km (600 mi) of a two-lane rural-road network from South Moravia, Czech Republic, were used. On the basis of 8 years of annual crash frequencies, together with exposure and geometrical variables, several variants of prediction models were developed. To study the quality of the models, a series of consistency tests was applied relative to comparison of the models themselves as well as to their diagnostic performance. As a result, simple crash prediction models (that included traffic volume, segment length, and curvature change rate) were found sufficient for network screening. If one supposes that length and curvature are unlikely to change often, only traffic volume data need to be periodically updated. Consistency analyses indicate that this period should be 4 years. Under these conditions, models are being applied in the studied region. Further planned activities include extensions to intersections and also to other Czech regions.