Individual station estimation from smart card transactions

doi:10.3969/j.issn.1000-5641.2017.05.018

Abstract

Abstract: With the fast development of public transportation network and widespread use of smart card, more and more rich semantic information about human mobility behaviors are hidden in smart card transaction data. However, a great number of current smart cards are initially designed for charging and do not record any detailed information about where and when a passenger gets on or gets off a bus, which brings out great difficulties for analyzing, mining transaction data and providing more precise location-based services. This paper presents Space-Time Adjacency algorithm (STA) and Historical Trip Based algorithm (HTB) to estimate the bus station of each card's transaction records with the aid of integral historical data including complete subway transaction data. Specifically, STA does the initial reconstruction work according to the space-time proximity of adjacent transaction records. Then HTB first cuts the collection of records to form trips that contain explicit trip purposes, then extracts taken lines and transfer lines using historical data, next generates candidate stations for each taken line, and finally uses them to recover the transaction records again. Experiments show that the proposed algorithms work well and narrow the range of candidate stations for bus lines, and have good time efficiency.

Key words: smart card, incomplete data, card mining, station estimation

CLC Number:

TP391

WANG Yi-lin, ZHANG Zhi-gang, JIN Che-qing. Individual station estimation from smart card transactions[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 201-212.

References

[1] LATHIA N, CAPRA L. How smart is your smartcard? Measuring travel behaviours, perceptions, and incentives[C]//Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 2011:291-300.
[2] LATHIA N, FROEHLICH J, CAPRA L. Mining public transport usage for personalised intelligent transport systems[C]//2010 IEEE 10th International Conference on Data Mining. IEEE, 2010:887-892.
[3] BAGCHI M, WHITE P R. The potential of public transport smart card data[J]. Transport Policy, 2005, 12(5):464-474.
[4] PELLETIER M P, TRÉPANIER M, MORENCY C. Smart card data use in public transit:A literature review[J]. Transportation Research Part C Emerging Technologies, 2011, 19(4):557-568.
[5] ZHANG F, YUAN N J, WANG Y, et al. Reconstructing individual mobility from smart card transactions:A collaborative space alignment approach[J]. Knowledge and Information Systems, 2015, 44(2):299-323.
[6] TRÉPANIER M, TRANCHANT N, CHAPLEAU R. Individual trip destina tion estimation in a transit smart card automated fare collection system[J]. Journal of Intelligent Transportation Systems Technology Planning & Operations, 2007, 11(1):1-14.
[7] WANG W, ATTANUCCI J P, WILSON N H M. Bus passenger origin-destination estimation and related analyses using automated data collection systems[J]. Journal of Public Transportation, 2010, 14(4):131-150.
[8] BARRY J, NEWHOUSER R, RAHBEE A, et al. Origin and destination estimation in New York City with automated fare system data[J]. Transportation Research Record, 2002, 1817:183-187.
[9] SONG C, QU Z, BLUMM N, et al. Limits of predictability in human mobility[J]. Science, 2010, 327:1018-1021.
[10] GIANNOTTI F, NANNI M, PEDRESCHI D, et al. Unveiling the complexity of human mobility by querying and mining massive trajectory data[J]. The VLDB Journal, 2011, 20(5):695-719.
[11] LI Z, DING B, HAN J, et al. Mining periodic behaviors for moving objects[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2010:1099-1108.
[12] WANG Y, YUAN N J, LIAN D, et al. Regularity and conformity:Location prediction using heterogeneous mobility data[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015:1275-1284.
[13] BALAN R K, NGUYEN K X, JIANG L. Real-time trip information service for a large taxi fleet[C]//Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services. ACM, 2011:99-112.
[14] DASH M, KOO K K, HOLLECZEK T, et al. From mobile phone data to transport network-gaining insight about human mobility[C]//IEEE International Conference on Mobile Data Management. IEEE, 2015:243-250.
[15] DAI J, YANG B, GUO C, et al. Personalized route recommendation using big trajectory data[C]//IEEE 31st International Conference on Data Engineering. IEEE, 2015:543-554.
[16] 龙瀛, 孙立君, 陶遂. 基于公共交通智能卡数据的城市研究综述[J]. 城市规划学刊, 2015, 3:70-77.
[17] LONG Y, THILL J C. Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J]. Computers Environment & Urban Systems, 2015, 53:19-35.
[18] EL-GENEIDY A, GRIMSRUD M, WASFI R, et al. New evidence on walking distances to transit stops:Identifying redundancies and gaps using variable service areas[J]. Transportation, 2014, 41(1):193-210.
[19] DANIELS R, MULLEY C. Explaining walking distance to public transport:The dominance of public transport supply[J]. Journal of Transport & Land Use, 2011, 6(2):5-20.
[20] CUI A. Bus passenger origin-destination matrix estimation using automated data collection systems[D]. Cambridge, MA:Massachusetts Institute of Technology, 2006.
[21] 胡继华, 邓俊, 黄泽. 结合出行链的公交IC卡乘客下车站点判断概率模型[J]. 交通运输系统工程与信息, 2014, 14(2):62-67.
[22] 上海市政府数据服务网.[DB/OL].[2017-05-20]. http://www.datashanghai.gov.cn.