Frequent-pattern growth algorithm based association rule mining method of public transport travel stability

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The accurate depiction and understanding of the travel behavior characteristics of public transport (PT) commuters is an important foundation for better improving PT service and encouraging car owners to use the sustainable and ecofriendly PT; and there are significant differences in the travel stability (TS) characteristic of PT commuters, developing methods for accurately measuring such differences is an issue. Therefore, smart card transaction data, line and stop data, and travel survey data from Beijing were collected, then individual travel chain information of commuting passengers was extracted using the associating and matching method. Thereafter, a multilevel characteristic indicator system including the number of nonhome activity points, commuting trip ratio, travel spatial equilibrium, time stability and departure time concentration was constructed to capture the individual TS. Moreover, an association rule mining model based on the frequent-pattern (FP) growth algorithm was developed by modeling the indicators as items and the PT-commuter TS as transactions. Thus, seven meaningful rules for revealing the internal relationships between individual travel characteristics and commuter TS were obtained, and PT commuters were classified into three groups according to the TS levels. Finally, a conceptual model of the mode shift to higher TS levels among commuters was developed, and some targeted measures for enhancing the TS levels of PT users were proposed. The findings are expected to provide new perspectives for travel behavior analysis and policy control to enhance and maintain passenger TS, also are conducive to increasing PT usage while reducing the usage of cars.