The availability of mobility options and corresponding infrastructure plays a crucial role in transportation choice. A comprehensive understanding of mobility demand and user needs is essential for developing effective services and infrastructure. The most common method for capturing mobility behavior is through travel diaries, where participants report details about their movements on a specific reference day, including trip purposes, modes of transport, travel times, and distances covered. This methodology, known as a cross-sectional study, proves effective when conducted on a large scale within the population, as exemplified by the "Mobilität in Deutschland" survey. Due to the effort and cost involved, these surveys typically take place every 6 to 9 years. Additionally, longitudinal studies aim to capture behavioral changes over time. While these methods provide valuable insights into trends and behavioral shifts, they are often demanding for participants, leading to high dropout rates. Both types of studies are resource-intensive, costly, and often yield incomplete or subjective data that lacks precision. This study simplifies mobility analysis by demonstrating that focusing on primary travel objectives effectively captures key aspects of mobility data. The investigation explores whether surveys limited to primary destinations provide representative data, enabling the development of a streamlined, low-cost survey tool that serves as a supplement in the intervals between comprehensive surveys like "Mobilität in Deutschland." Such a tool proves especially beneficial for smaller municipalities and cities with limited resources. A straightforward mathematical model describes mobility behaviors accurately, using robust data to provide a generalized and simplified representation of human mobility. To achieve these objectives, a digital mobility survey gathers precise, long-term data with minimal participant effort. The survey includes 284 participants, comprising 208 from the University of the Bundeswehr Munich and 76 from a control group. Leveraging advancements in technology, the survey utilizes a tracking app that automatically and objectively captures participants’ movements, identifying transportation modes with minimal manual input. This approach enables extended data collection periods while maintaining high accuracy and detail. Notably, 240 participants actively engaged for more than six days. Data collection, conducted between March 2022 and August 2023, records 105,778 trips and 221,088 tracks, with a median user engagement duration of 38 days. Systematic data management processes, including aggregation, cleaning, and clustering, efficiently extract meaningful patterns from repetitive behaviors. These patterns are then described mathematically using an exponential decay model, which captures the declining importance of less frequently visited locations. The study introduces a frequency-based clustering approach that segments users according to their repetition behavior, revealing behavioral profiles with different mobility tendencies. The findings show that visit frequency not only explains general movement patterns but also serves as a reliable predictor of transport mode choice. Additionally, to simplify the survey, the study evaluates the reliability of respondents' answers regarding general mode choice. Although this approach, focusing on typical or habitual usage rather than specific trips, has been applied in practice, its accuracy has not been systematically assessed. By comparing self-reported responses with tracking data, the study shows that typical mode choice generally reflects actual usage, despite some variation across transport modes. Finally, the methodology is applied to data from a benchmark survey, and a comprehensive evaluation of the dataset’s strengths and limitations demonstrates its effectiveness in supporting mobility pattern analysis.     «

The availability of mobility options and corresponding infrastructure plays a crucial role in transportation choice. A comprehensive understanding of mobility demand and user needs is essential for developing effective services and infrastructure. The most common method for capturing mobility behavior is through travel diaries, where participants report details about their movements on a specific reference day, including trip purposes, modes of transport, travel times, and distances covered. Thi...     »