Traffic planners in most cities need detailed bicycle route data to investigate cycling behavior. This disaggregate data, which provides information on revealed preference of cyclists choosing their routes through a city road network, is often used to analyze bicycle route choice. However, the required data is usually not available for most city areas. In recebt years, more and more commercial companies and nongovernmental initiatives provide aggregate GPS-based cycling data. Due to crowdsourcing, the data is relatively cheap to acquire and available for most cities around the globe (e.g. data from Strava). However, the data do not provide detailed information on single routes because companies usually process the data and provide aggregate data instead of single route data. Thus, the data do not meet the requirements for detailed analysis. Few studies investigated how to exploit the aggregate data or even how to derive single routes. Disaggregating the available aggregate data to synthetic single routes could help to generate detailed cycling route data on low costs. However, there is currently no knowledge about feasibility of route disaggregation and the validity of resulting routes. Therefore, the article presents results of the evaluation of a developed route synthetization approach. To evaluate the approach, a large bicycle GPS data sample is aggregated first. This ensures that the used aggregate data possess the same data structure as the data provided e.g. by commercial providers. In a second step, detailed routes are synthesized using a state-of-research multi-step route synthetization approach. The comparison of synthesized routes with the original ones reveals an impressive match (up to 97%). However, accuracy strongly depends on zonal size of the aggregate input data.