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Periodic pattern mining is of great significance for understanding passenger travel behavior, but the previous works mainly focused on the trajectory data and the dimension of the spot/point. Besides, many uncertain factors (severe weather, traffic accident, etc.) may interfere with discovering original and accurate periodic travel patterns. This paper proposes a novel type of travel pattern called motif periodic frequent pattern (MPFP), which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics, considering, uncertain disturbances. We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data (SCD), and apply the real long-time-span experimental data from a large-scale metro system is applied. Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of “week”. Only the top 10 types of all 4 624 types account for about 95% of all motifs and the top 5 types constitute about 90%, and the MPFP of the top 3 types of motifs account for nearly 80% of all periodic patterns, in which Mono-MPFP and 2-MPFP are the main ones. The relatively stable time range of MPFP is three months, and the threshold for the optimal uncertain disturbance factor should be set at 5%. Additionally, several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure. Although conventional algorithms for periodic frequent pattern detection have numerous applications, there is still little research on periodic frequent pattern detection of individual passengers in the metro. The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network, which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data. This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes (PFPTS). This discovered pattern can automatically capture the features of the temporal dimension (morning and evening peak hours, week) and the spatial dimension (entering and leaving stations). The corresponding complete mining algorithm with the PFPTS-tree structure has been developed. To evaluate the performance of PFPTS-tree, several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network. The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.