Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Road networks are a vital national property however, with the passage of time their condition deteriorates. It is critical for a road agency to know the various factors such as road conditions, traffic conditions, environmental conditions that affect the Road Pavement Deflection values. This paper proposes a data analytic application for assessing the road pavement condition. The data analytics process includes acquisition and integration of data from multiple sources, pre-processing the data and mining the useful information from the data. The generated data mining models are able to demonstrate factors that affect pavement deflection data. Outputs of this research inform the road managers about the road conditions and enable them in formulating an efficient e-government policy for budget and maintenance of this road asset.

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study.