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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.

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Driving speed is an important risk factor, especially when negotiating horizontal curves. Therefore it may be useful in extracting surrogate measures to proactively safety assessment, a practice consistent with a current shift towards a Safe System approach to addressing road trauma. Review of previous literature indicated two categories of studies: (1) studies focusing on a safe driving perspective, i.e. studies primarily interested in finding the cut-off point in FCD data characteristics between safe and unsafe driving; (2) studies focusing on relating meaningful risk rates (percentages of exceeding the risk thresholds) to specific locations, and thus identify safety critical sites. However, no study was found that specifically focused on the relationship between kinematic characteristics (other than just speed) and road curves. The presented study focused on exploring the relationship between acceleration and jerk thresholds and crashes occurring on road curves. The first objective was to determine meaningful acceleration and jerk thresholds to utilize in explaining safety performance when negotiating curves. For this purpose floating car data (FCD) from a fleet of company vehicles, driving in rural sections of national roads in the Czech Republic, was collected and used to derive and validate potential surrogate safety measures. FCD presents in-vehicle information with several benefits compared to traditional techniques, such as feasibility of data collection, relatively unlimited spatial coverage, and availability of historical data. In the analysis, lateral acceleration and longitudinal jerk were found to be the most influential measures of curve safety performance. To sum up, the exploratory study outlined a practical approach to proactive evaluation of road curve safety: FCD data can generate useful surrogate measures of curve safety (acceleration and jerks) associated with crash history. A larger study is required to strengthen robustness of the results and provide confidence necessary for practical application. Potential use cases may include conducting interim evaluations of curve road safety treatments, or in-vehicle monitoring devices for detection of potentially unsafe manoeuvers and providing real-time feedback to drivers based on a combination of identified safety thresholds.

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