Abstract: Individual travel behaviors demonstrate a degree of regularity, particularly concerning time, space, and frequency repetition. Measuring the regularity of individual travel behavior contributes to understanding variations among individuals and provides opportunities for personalized transportation services. However, previous measurement methods have not comprehensively addressed these three aspects simultaneously or sufficiently captured the repetition of "basic travel behaviors", while also relying on diverse data types without a comprehensive methodology covering multiple datasets. To address these research gaps, this paper presents an innovative method for measuring the regularity of individual users, which considers the repetition of basic travel behaviors along with temporal, spatial, and frequency dimensions. Applying this method to historical trip data from bike-sharing and subway systems has illustrated the effectiveness and applicability of the approach proposed in this paper. The findings suggest the broad applicability of this method across various data types, provided that the data allows for the extraction of information regarding users' travel origins and destinations. Moreover, this method effectively produces a regularity value to assess and compare the overall regularity of users, surpassing previous limitations that solely relied on using frequency as an initial filter or distinguishing users as regular or irregular based solely on frequency thresholds.