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

机译：北京城市轨道运输中旅行的多维频繁挖掘

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摘要

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.
机译：北京以高人口密度和工作住房不匹配的显着现象而闻名，这里的居民具有特定的旅行方式，特别是在北京城市轨道运输（Burt）中。多维频繁模式挖掘（MFPM）可以有效地挖掘居民的旅行行为的频繁模式，找出伯特的跳闸协会规则，并探索陷入困境机制。从博尔特传输卡数据的旅行信息是在2015年获得的，并且在空间，时间和线尺寸中选择了数据作为属性集。它发现有一些土地利用相关的“站集团”，如“西尔齐集团”和“国都集团”，以及几个强烈相关的“通勤od对”，如{王晶，玛正平}和{昌阳，丰台}。该研究将有助于解开伯特和协助运营管理的骑手的旅行规律。

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