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首页 > 过刊浏览>2017年第28卷第6期 >1498-1515. DOI:10.13328/j.cnki.jos.005220
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基于车牌识别流数据的车辆伴随模式发现方法
作者:
基金项目:

国家自然科学基金（61672042）；北京市市委组织部、北京市优秀人才培养资助，青年骨干个人项目；北方工业大学“人才强校计划”青年拔尖人才培育计划


Approach to Discover Companion Pattern Based on ANPR Data Stream
Author:
Fund Project:

National Natural Science Foundation of China (61672042); Program for Youth Backbone Individual, Beijing Municipal Party Committee Organization Department; Training Plan of Top Young Talent in North China University of Technology

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    摘要:

    针对伴随车辆检测这一新兴的智能交通应用，在一种特殊的流式时空大数据——车牌识别流式大数据（ANPR）下，重新定义了Platoon伴随模式，提出PlatoonFinder算法，即时地在车牌识别数据流上挖掘Platoon伴随模式.主要贡献包括：第一，将Platoon伴随模式发现问题映射为数据流上的带有时空约束的频繁序列挖掘问题，与传统频繁序列挖掘算法仅考虑序列元素之间位置关系不同，该算法能够在频繁序列挖掘的过程中有效处理序列元素之间复杂的时空约束关系；第二，该算法融入了伪投影等性能优化技术，针对数据流的特点进行了性能优化，能够有效应对车牌识别流式大数据的速率和规模，从而实现车辆Platoon伴随模式的即时发现.通过在真实车牌识别数据集上的实验分析表明：PlatoonFinder算法的平均延时显著低于经典的Aprior和PrefixSpan等频繁模式挖掘算法，也低于真实情况下交通摄像头的车牌识别最小时间间隔.因此，所提出的算法可以有效地发现伴随车辆组及其移动模式.

    Abstract:

    Companion vehicle discovery is a newly emerging intelligent transportation application. Aiming at it, this paper redefines the Platoon companion pattern over a special type of spatio-temporal data stream, or ANPR (automatic number plate recognition data). Accordingly, a PlatoonFinder algorithm is also proposed to mine Platoon companions over ANPR data stream instantly. First, Platoon discovery problem is transformed into frequent sequence mining problem with customized spatio-temporal constraints. Compared to traditional frequent sequence mining algorithms, this new algorithm can effectively handle complex spatio-temporal relationships among sequence elements rather than their positions. Second, the new algorithm also integrates several optimization techniques such as pseudo projection to greatly improve the efficiency. It can efficiently deal with high speed and large scale ANPR data stream so as to instantly discover Platoon companions. Experiments show that the latency of the algorithm is significantly lower than classic frequent pattern mining algorithms including Apriori and Prefixspan. Furthermore, it is also lower than the minimum time interval between any two real ANPR data records. Hence, the proposed algorithm can discover Platoon companions effectively and efficiently.

    参考文献
    [1] Laube P, Imfeld S. Analyzing relative motion within groups of trackable moving point objects. In:Proc. of the 2nd Int'l Conf. on Advances in Geographic Information Systems (GIScience). Berlin:Springer-Verlag, 2002. 132-144.[doi:10.1007/3-540-45799- 2_10]
    [2] Gudmundsson J, Van Kreveld M. Computing longest duration flocks in trajectory data. In:Proc. of the 14th Annual ACM Int'l Symp. on Advances in Geographic Information Systems (ACM-GIS). New York:Association for Computing Machinery, 2006. 35-42.[doi:10.1145/1183471.1183479]
    [3] Vieira MR, Bakalov P, Tsotras VJ. On-Line discovery of flock patterns in spatio-temporal data. In:Proc. of the 17th ACM Int'l Symp. on Advances in Geographic Information Systems (ACM SIGSPATIAL). New York:Association for Computing Machinery, 2009. 286-295.[doi:10.1145/1653771.1653812]
    [4] Jeung H, Shen HT, Zhou XF. Convoy queries in spatio-temporal databases. In:Proc. of the IEEE Int'l Conf. on Data Engineering (ICDE). Washington:IEEE Computer Society, 2008. 1457-1459.[doi:10.1109/ICDE.2008.4497588]
    [5] Jeung H, Yiu ML, Zhou XF, Jensen CS, Shen HT. Discovery of convoys in trajectory databases. Proc. of of the VLDB Endowment, 2008,1(1):1068-1080.[doi:10.14778/1453856.1453971]
    [6] Li ZH, Ding BL, Han JW, Kays R. Swarm:Mining relaxed temporal moving object clusters. Proc. of of the VLDB Endowment, 2010,3(1):723-734.[doi:10.14778/1920841.1920934]
    [7] Tang LA, Zheng Y, Yuan J, Han JW, Leung A, Peng WC, Porta TL. A framework of traveling companion discovery on trajectory data streams. ACM Trans. on Intelligent Systems & Technology, 2013,5(1):992-999.[doi:10.1145/2542182.2542185]
    [8] Li YX, Bailey J, Kulik L. Efficient mining of platoon patterns in trajectory databases☆. Data and Knowledge Engineering, 2015, 100(PA):167-187.[doi:10.1016/j.datak.2015.02.001]
    [9] Han YB, Wang GL, Yu J, Liu C, Zhang ZM, Zhu ML. A service-based approach to traffic sensor data integration and analysis to support community-wide green commute in China. IEEE Trans. on Intelligent Transportation Systems, 2015,17(9):1-10.[doi:10.1109/TITS.2015.2498178]
    [10] Zhu ML, Liu C, Wang JW, Wang XB, Han YB. Instant discovery of moment companion vehicles from big streaming traffic data. In:Proc. of the Int'l Conf. on Cloud Computing and Big Data (CCBD). Piscataway:IEEE, 2015. 73-80.[doi:10.1109/CCBD.2015.65]
    [11] Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. In:Proc. of the 9th Int'l Conf. on Advances in Spatial and Temporal Databases (SSTD). Berlin:Springer-Verlag, 2005. 364-381.[doi:10.1007/11535331_21]
    [12] Li YF, Han JW, Yang J. Clustering moving objects. In:Proc. of the 10th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD). New York:Association for Computing Machinery, 2004. 617-622.[doi:10.1145/1014052.1014129]
    [13] Kriegel HP, Pfeifle M. Density-Based clustering of uncertain data. In:Proc. of the ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD). New York:Association for Computing Machinery, 2005. 672-677.[doi:10.1145/1081870. 1081955]
    [14] Jensen CS, Lin D, Ooi BC. Continuous clustering of moving objects. IEEE Trans. on Knowledge and Data Engineering, 2007,19(9):1161-1174.[doi:10.1109/TKDE.2007.1054]
    [15] Zheng K, Zheng Y, Yuan NJ, Shang S. Online discovery of gathering patterns from trajectories. In:Proc. of the 29th Int'l Conf. on Data Engineering (ICDE). Washington:IEEE Computer Society, 2013. 242-253.[doi:10.1109/ICDE.2013.6544829]
    [16] Zhang JM, Li JL, Wang SG, Liu ZH, Yuan Q, Yang FC. On retrieving moving objects gathering patterns from trajectory data via spatio-temporal graph. In:Proc. of the IEEE Int'l Congress on Big Data (BIGDATACONGRESS). Piscataway:IEEE, 2014. 390-397.[doi:10.1109/BigData.Congress.2014.64]
    [17] Yoo JS, Boulware D, Kimmey D. A parallel spatial co-location mining algorithm based on mapreduce. In:Proc. of the IEEE Int'l Congress on Big Data (BIGDATACONGRESS). Piscataway:IEEE, 2014. 25-31.[doi:10.1109/BigData.Congress.2014.14]
    [18] Yu YW, Wang Q, Wang XD. Continuous clustering trajectory stream of moving objects. China Communications, 2013,10(9):120-129.[doi:10.1109/CC.2013.6623510]
    [19] Yu YW, Wang Q, Wang XD, Wang H, He J. Online clustering for trajectory data stream of moving objects. Computer Science and Information Systems, 2013,10(3):1293-1317.[doi:10.2298/CSIS120723049Y]
    [20] Agrawal R, Srikant R. Mining sequential patterns. In:Proc. of the IEEE Int'l Conf. on Data Engineering (ICDE). Washington:IEEE Computer Society, 1995. 3-14.
    [21] Srikant R, Agrawal R. Mining sequential patterns:Generalizations and performance improvements. In:Proc. of the Advances in Database Technology (EDBT), Vol.1507. Berlin:Springer-Verlag, 1996. 1-17.[doi:10.1007/BFb0014140]
    [22] Zaki MJ. SPADE:An efficient algorithm for mining frequent sequences. Machine Learning, 2001,42(1):31-60.[doi:10.1023/A:1007652502315]
    [23] Ayres J, Flannick J, Gehrke J, Yiu T. Sequential pattern mining using a bitmap representation. In:Proc. of the 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD). New York:Association for Computing Machinery, 2002. 429-435.[doi:10.1145/775047.775109]
    [24] Han JW, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC. FreeSpan:Frequent pattern-projected sequential pattern mining. In:Proc. of the ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD). New York:Association for Computing Machinery, 2000. 355-359.[doi:10.1145/347090.347167]
    [25] Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC. PrefixSpan:Mining sequential patterns efficiently by prefixprojected pattern growth. In:Proc. of the 17th Int'l Conf. on Data Engineering (ICD?圩??婗桡敳湨杩?卧???娺桉畅?婅堠????????愠獓敯摣?灥慴特愬氠氲攰氰?洮椠渲椱渵札′漲昴?晛牤敯煩町攱渰琮?挱氰漹猯敉摃?獅攮焲田攰渱挮改猱?眸椳琰桝?浢慲瀾牛攲搶畝挠教???湘?倬爠潈捡??潊晗?琠桁敦???瑲栠??渠瑃?汯??潡湮昺??潮湩??氠杣潬牯楳瑥桤洠獳?慱湵摥??物捡桬椠瑰敡捴瑴略牲敮獳?晩潮爠?偡慲牧慥氠汤敡汴?偳牥潴捳攮猠獉楮渺材??????偦倠????攲爰氰椳渠?卉灁牍椠湉杮整爧?嘠敃牯汮慦朮????????ㄠ???????嬨摓潄楍?????どぬ???????????????????弮ㄠ?崶?戭爱?嬷??嵤??攺猱猰氮?刱??倯爱漮戹愷戸椱氶椱猱琹椷挲?猳琳愮琱椵捝?汢潲愾摛?户慝氠慗湡据楧渠杊?漬映?灡慮爠慊汗氬攠汌?洠楃渮椠湆杲?潱晵?普牴攠煣畬敯湳瑥?猠敳煥畱敵湥据散獥???????吠牷慩湴獨??潴渠??湮潤睩汤敡摴来攠?慡湩摮??慮瑡慮??渮朠楉湅故故爠楔湲条??金?????????????????ㄠ??孴摡漠楅??ど???ひ??呧????日????㈨?ㄩ??日名崲?戱爰?嬶??嵤??漺漱渰攮礱?????剋潄摅搮椲挰欰?????匳敝焼畢敲渾瑛椲愸汝?灈慥瑬瑥敮爠湐?洠楈湡楮渠杊??瀠灐牥潩愠捊栬攠獗?慮湧搠?愬氠权潨牥楮琠桑浍猬???????潕洮瀠畍瑵楬湴杩?卄畩牭癥敮祳獩???ぬㄠ???????????????孥摲潮椠??の?????????????????????崱?扴牨?孉??崧??桃慯湮杦??????敮敦?坲卭???楯湮搠楡湮杤?牋敮捯敷湬瑥?晧牥攠煍畡敮湡瑧?業瑥敮浴猠攨瑃獉?態搩愮瀠瑎楥癷攠汙祯?潫瘺敁牳?潯湣汩楡湴敩?摮愠瑦慯?猠瑃牯敭慰浵獴???渠?偡牣潨捩??潲晹?琠栲攰‰?琮栠??????湛瑤?汩??漰渮映??漴渵??渰漲眵永攵搮朵攰′?椰猰捝漼癢敲爾祛′愹湝搠??慩琠慊??楈湡楮渠杊??匠????????乃敯睮?奴潲牡歩??猭獂潡捳楥慤琠楳潥湱?晥潮牴??潬洠灰畡瑴楴湥杲??慭捩桮楩湮敧爺祔??㈠ばち???????????孨搠潭楥??は?????????????????ぴ?嵬?扩牧?孮??嵉??敯畲湭条??卯???桹慳湴?六????匰吰爷攬攲??′琩爺攱攳″猭琱父田挮瑛畤牯敩?昱漰爮?琰栰攷?浳椱渰椸渴朴?漰昰?昭爰攰焰甶攭湺瑝?獢敲琾獛″昰牝漠浃?摵慥瑨愠?獅琮爠敍慩浮獩???湴?偲牧潥捴??潲晩?瑮桴敥??瑳桥???????湬琠?污??潥湲普?漠湷??慨琠慴??楥渭楩湮杴?????????偮楴猧捬愠瑊慯睵慲祮????????ねば????㈠??????孥搠漦椠??て???ち????????との???水崠?戰爱?嬬?〨崴??椱?????愳椮敛牤????吰甮昵琱攲????偣慳灩慴搮椲洰漱猰?嘲??吰畝挼止敲爾?倳???乄潥?灩慲湩敺??渮漠?来慢楓湐??晅昺楁挠楰敡湲瑡?敬癥慬氠畳慥瑱極潥湮?潥映?獩汮楩摮楧渠条?睧楯湲摩潴睨?愠杴杯爠敡杮慡瑬敹獺?漠癗敥牢?摬慯瑧愠?獡瑴牡攮愠浉獮??卲???传??删整捨潥爠摉????ぉ??????????????孄摡潴楡??ど???????ぃ??ㄩ????び?????嵡?戺牉?孅??崠′?漰稲愮映愷爵椵????吮桛慤歯歩愺爱‰???娰愹港楉潃汄潍???‰嘲攮爱椱昸礴椰渴朶?愼湢摲￣浛椳渲楝渠杇?晲牡敬煮畩敫渠瑖?瀠態瑡瑲敹牰湩獳?晇爮漠浐?汲慡牬杬敥?眠楴湲摥潥眭獰?潯癪敥牣?摩慯瑮愭?獡瑳牥敤愠浳獥???湮?健爠潭捩??潮晧?瑡桬敧???瑴桨??渆琮?汐??潡湬晬?潬渠??慭瑰慵??湮杧椬渠攲攰爰椴測朳‰????????圴愷猲栮楛湤杯瑩漺渱???????潪洮灰畡瑲散牯?匲漰挰椴攮琰礳?‰日?そ???????????嬠摃潘椬??ど????????????ぬ???????????嵦or mining frequent closed sequences. In:Proc. of the Workshop on Autonomous Intelligent Systems:Agents and Data Mining (AIS-ADM). Berlin:Springer-Verlag, 2005. 184-192.[doi:10.1007/11492870_15]
    [34] Qiao SJ, Tang CJ, Dai SC, Zhu MF, Peng J, Li HJ, Ku YC. PartSpan:Parallel sequence mining of trajectory patterns. In:Proc. of the 5th Int'l Conf. on Fuzzy Systems and Knowledge Discovery (FSKD). Washington:IEEE Computer Society, 2008. 363-367.[doi:10.1109/FSKD.2008.33]
    [35] Yu DJ, Wu???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
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朱美玲,刘晨,王雄斌,韩燕波.基于车牌识别流数据的车辆伴随模式发现方法.软件学报,2017,28(6):1498-1515

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  • 收稿日期:2016-05-07
  • 最后修改日期:2016-07-15
  • 在线发布日期: 2017-02-21
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