In recent years, awareness of the methodological implications of hierarchical data structures on statistics has noticeably increased in the field of travel behavior research based on Household Travel Surveys (HTSs). Addressing methodological challenges such as data harmonization, the influence of hierarchically structured data on the analysis, and the effects of survey-specific elements like clustering on accuracy is essential from both methodological and analytical perspectives. This study deals with both aspects using two large-scale repeated cross-sectional HTSs from Germany. The results strongly advocate undertaking the effort of data harmonization and fusion as a highly promising approach to increase the utility of these surveys. Furthermore, it is highly recommended to account for the hierarchical data structure of HTSs using multi-level modeling techniques. We find no evidence that the application of weights in statistical explanatory models for car use significantly affects the model results. In terms of travel behavior, our results largely confirm previous studies: car availability on the reporting day is the strongest factor in increasing car use, while the availability of a public transport season ticket has the greatest effect on reducing it.