The quality and provenance of Transport Data has often been understated when delivered to a transport model, yet the rigorous theoretical nature of these models can be undermined easily when used with data of dubious quality.

But transport models are greedy consumers of information; often difficult, but always expensive to collect. Indeed, it is not uncommon for it to cost more to collect and process the data than the modelling exercise itself. Data is a valuable resource that is often under utilised; mostly because once collected for its original use there is little incentive to devote resources to cataloging and storing it so it can be of material use in the future.

This is nothing new, and methodologies have evolved successfully to synthesise data using best available information. But even these techniques require access to the 'best quality' available partial data, otherwise the synthesis is based on sand.

Minnerva take the view that much can be done to improve on this situation, often by taking a wider view of immediate data project objective and undertaking the task within a clearly defined framework that offers downstream benefits with minimal marginal effort. This includes making sure that data is of the highest quality, that it is reusable, and that the processes used in its generation are not lost in time.

This covers many areas of the traditional transport data 'environment, such as:

• sampling methodologies
• household, roadside and PT travel surveys
• geocoding
• data validation and generation
• data synthesis

You can explore some of the projects that Minnerva have been involved in from the list on the right.