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Long-distance passenger transport shows considerable growth rates in recent years. This development results from changing individual behaviour in terms of trip making and mode choice. Existing aggregated data sources are insufficient to identify the driving forces behind these processes and will not allow a derivation of sound predictions for the years to come. Longitudinal microscopic data that capture long-distance journeys on a sufficient detailed level, as well as the characteristics and motives of related travellers is not widespread. For Germany we were able to fill this gap through an extensive survey during a three-year research project. Conventional cross-sectional surveys as used in most NTS are unable to generate the required complex and detailed information. Within the INVERMO project a multi-stage survey design was developed that joined cross-sectional, longitudinal, panel and stated preference methods to overcome the known drawbacks of each single approach. Furthermore we combined CATI, PAPI, retrospective and prospective techniques as well as inter-related stratified samples. Additionally the set of linked survey results allowed bidirectional re-weightings of the information gathered. In this paper we are going to present the employed methodology and some key findings that emphasise the need for combining survey techniques. A comparison of key figures with other survey results shows the reliability and validity of the approach.
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