One of the objectives of the Department of Transportation (DoT) in the Netherlands is to provide optimal information about traffic conditions to road users and traffic managers. The usefulness of this information greatly depends on the quality of the underlying traffic data. Traffic data are generated in a chain of applications, and it is well-known that in each processing step, data can get lost or get corrupted. Missing data are easy to detect, but corrupt data are not. So far no application was available that can check large amounts of data for outliers, and make an automated distinction between corrupt data and traffic-related outliers (e.g. because of accidents). The Da Vinci project, as described in this paper, aims at developing an application that can detect corrupt data and assist the specialist in analyzing the source of the error, so that the problem can be solved quickly. An important key to the success of Da Vinci is that different validation models are used together to achieve optimal results. The models have access to the data from each processing station of the information chain. Furthermore, an analyzing module was built to help the specialist making case-specific drill-downs in the underlying data in order to find the source of the corruption. A prototype of Da Vinci has been operational since spring 2007 and has successfully proven its usefulness. Different examples are presented in this paper showing how Da Vinci can detect corrupt data and help to find the source of the error.
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