Travel behavior and especially travel demand modeling are used to evaluate and predict the development of transport policies and transport measures in practice, to tackle more comprehensive issues such as climate change and mobility change. This work provides a structural and statistical methodology for mobility behavior, using comprehensive survey data from four countries, i.e., the US, UK, DE, and CH, from the 1970s up to 2018. For mobility behavior, we propose a framework for personalized daily mobility behavior, classified according to the number of modes of transport used throughout a day by a traveler. This classification schema is identical to the innate structure of the trip database; therefore, the deducted mobility behavior can be directly analyzed without any prior assumptions. The analysis uses linear regression techniques in relation to daily travel time, distance, and average travel speed. For the analysis, only the four most used modes of transport have been used, walking, cycling, car driver, and rail train, which are also furthest apart in terms of the indicators. These fundamental mobility indicators - also compared to the standard travel time budget - are found to be statistically stable and, being based on ANOVA results, expose long-term mobility patterns. Since behavior models are often parametrized and validated only under restrictive conditions, the developed approach can serve as a solid basis for effective travel behavior modeling. It can also assess and compare past, current, and future mobility behavior to identify effective transport measures for mobility change and to counter climate change.