Multi-Dimensional Frequent Pattern Mining of Trips in Beijing Urban Rail Transit

Beijing is famous for high population density and the significant phenomenon of job-housing mismatch, and residents here have specific mode of travel, especially in the Beijing urban rail transit (BURT). Multi-dimensional frequent pattern mining (MFPM) can efficiently mine the frequent patterns of residents' travel behavior, find out the association rules of trips in BURT, and explore the underling mechanism. Travel information from the transit card data of BURT was obtained in 2015 and chose data in spatial, temporal, and line dimensions as the attribute sets. It's found that there is some land use related "station group" such as "Xierqi Group" and "Guomao Group" and several strongly associated "commuting OD pairs" such as {Wangjing, Maquanying} and {Changyang, Fengtai}. The research will help unraveling the travel regularities of riders in BURT and assisting operation management.