Stuby on the Activity Patterns and Regularity of Public Transport Passengers

doi:10.12141/j.issn.1000-565X.220658

Abstract

Abstract:

In order to explore the activity pattern and regularity of public transport passengers, this study constructed multi-day passenger travel activity sequences using three weeks smart card data in Beijing in October 2020. The frequent activity pattern sequences of passengers were mined through the PrefixSpan algorithm, and the similarity measure method of activity patterns was defined based on the longest common subsequence. The day-to-day activity sequence similarity of individual and activity pattern similarities among different passengers were calculated respectively, and passengers were classified according to activity pattern similarities among passengers by using the hierarchical clustering algorithm. The results show that the similarity between workdays and weekends is significantly lower than that within workdays or weekends. In workdays, the activity sequence similarity between Friday and the other days is low. Meanwhile, the activity sequence similarity of the same days in different weeks is high. The result of hierarchical clustering shows that there are four typical activity patterns, including entertainment and shopping orientation, life orientation, work orientation and personal affair orientation. Moreover, the day-to-day activity sequence similarity of passenger with work orientation pattern is higher than that of passenger with other activity patterns. The research results in this paper are helpful to scientifically formulate accurate public transport operation management and service policies.

Key words: public transportation, passenger activity sequence, frequent sequence mining, sequence similarity, activity pattern 责任编辑：许花桃

CLC Number:

U491

CHEN Yanyan, WANG Zifan, SUN Haodong, et al. Stuby on the Activity Patterns and Regularity of Public Transport Passengers[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 40-50.

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活动类型	POI类别
个人事务	公司企业、科教文化服务、金融保险服务、政府机构及社会团体、医疗保健服务
其他生活外出	商务住宅、生活服务
购物	购物服务
休闲娱乐	餐饮服务、体育休闲服务、风景名胜

日期	活动序列
2020-10-12	〈H-W-H〉
2020-10-13	〈H-W-A-H〉
2020-10-14	〈H-W-H〉
2020-10-15	〈H-W-S-H〉
2020-10-17	〈H-R-S-H〉

类别1（娱乐购物导向）		类别2（生活外出导向）		类别3（工作通勤导向）		类别4（个人事务外出导向）
频繁活动模式序列	占比/%	频繁活动模式序列	占比/%	频繁活动模式序列	占比/%	频繁活动模式序列	占比/%
H-R-H	50.7	H-L-H	65.6	H-W-H	74.2	H-A-H	65.4
H-S-H	17.3	H-R-H	8.3	H-R-H	10.6	H-S-H	12.6
H-O-H	12.4	H-A-H	7.1	H-A-H	4.0	H-O-H	11.0
H-A-H	11.1	H-S-H	4.6	H-S-H	2.9	H-R-H	5.3
H-L-H	3.2	H-L-R-H	4.6	H-W-R-H	2.8	H-R-A-H	1.2
H-A-R-H	1.3	H-O-H	3.7	H-O-H	2.2	H-L-H	1.0
H-R-S-H	1.1	H-R-L-H	2.5	H-W-A-H	1.1	H-A-R-H	1.0
H-R-A-H	1.1	H-L-H-L-H	1.2	H-R-W-H	1.0	H-A-S-H	0.9
H-S-R-H	1.0	H-L-S-H	1.2	H-W-H-W-H	0.7	H-O-A-H	0.9
H-R-O-H	0.8	H-W-H	1.2	H-L-H	0.7	H-A-O-H	0.7

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