Analysis of Spatial Travel Association Rules for Rail Transit Based on AFC and POI Data
Abstract
In order to explore the spatial distribution rules and causes of urban rail transit passenger travel, this paper mines the spatial 1-frequent itemset and 2-frequent itemsets of weekdays and weekends metro passenger travel based on Apriori algorithm using the continuous week of Automatic Fare Collection System (AFC) swipe card. At the same time, the K-Means algorithm is used to cluster the subway stations and explore the causes of association rules by combining the Point of Interest (POI) data of the same period within the radiation range of the subway stations. The study shows that the spatial distribution pattern of inbound and outbound passenger flow of Shanghai rail transit is consistent between weekdays and weekends, and the outbound passenger flow is more concentrated than the inbound passenger flow, and the significance of weekends is higher; the spatial distribution of metro stations is "circled"; the analysis of the high-lift association rules show that a large passenger flow group centered on the type 3 station is formed in the spatial location, and the passenger flow within the group is mainly commuter flow with separation of employment and residence. The association rule mining of metro passenger travel data is beneficial to understanding the spatial distribution pattern and causes of metro ridership, which can provide reference for rail network planning and operation management.
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Xu ZZ. Current situation and development trend of urban public transportation characteristics in China[J]. People's Public Transport, 2022, No.154(10):10-19.
Liang Q, Weng JC, Zhou W, Rong J. Crowd identification of public transportation commuting stability based on association rules[J]. Journal of Jilin University (Engineering Edition), 2019, 49(05): 1484-1491.
Guo X, David Z.W. Wang, Wu JJ, Sun HJ, Zhou L. Mining commuting behavior of urban rail transit network by using association rules[J]. Physica A: Statistical Mechanics and its Applications, 2020,559.
Chu F. Research on the characteristics of Beijing rail transportation based on association rule mining[D]. Kunming University of Science and Technology, 2020.
Xu Y, Ji X, Jin Z. What travel scenarios are the opportunities of car sharing? [J]. Plos one, 2021, 16(12): e0260605.
Shen P, Ouyang L, Wang C, et al. Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data[J]. Geo-spatial Information Science, 2020, 23(4): 352-361.
Wang Z, Ma D, Sun D, et al. Identification and analysis of urban functional area in Hangzhou based on OSM and POI data[J]. PLoS one, 2021, 16(5): e0251988.
Xu W, Zheng CJ, Ma GH. et al. Research on classification of urban rail transit stations based on k-means clustering[J]. Journal of Guizhou University (Natural Science Edition), 2018, 35(06): 106-111.
Xia X, Gai JY. Classification and passenger flow characteristics analysis of urban rail transit stations based on K-Means clustering algorithm[J]. Modern Urban Rail Transit, 2021(04):112-118.
Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases[J].ACM SIGMOD Record, 1993, 22(2): 207-216.
Cai WJ, Zhang XH, Zhu JQ. et al. A review of association rule mining[J]. Computer Engineering,2001(05):31-33+49.
Agrawal R, Srikant R. Fast algorithms for mining association rules[C]//Proc. 20th int. conf. very large databases, VLDB. 1994, 1215: 487-499.
He Z, Huang HK, Tian SF. A discovery method for optimizing correlation rules[J]. Journal of Computer Science, 2006(06): 906-913.
Brin S, Motwani R, Silverstein C. Beyond market baskets: generalizing association rules to correlations[C]// Proceedings of the 1997 ACM SIGMOD international conference on Management of data. 1997: 265-276.
MAC Queen J. Some methods for classification and analysis of multivariate observations [C]// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967:281-297.
Yang JB, Zhao C. A review of research on K-Means clustering algorithm[J]. Computer Engineering and Applications,2019,55(23):7-14+63.
Ministry of Housing and Urban-Rural Development of the People's Republic of China. Letter of Construction Regulations [2015] 276 Notice of the Ministry of Housing and Urban-Rural Development on the Issuance of Planning and Design Guidelines for Areas Along Urban Railways [Z]. 2015.
DOI: https://doi.org/10.18686/utc.v9i3.193
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