Similar trajectory query method based on massive vehicle license plate recognition data

doi:10.16511/j.cnki.qhdxxb.2017.22.018

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Abstract Vehicle license plate recognition data provides a kind of traffic monitoring data that is a large spatial-temporal stream with fixed positions. Similar trajectory queries of such data face several problems. This paper presents a similar trajectory query method based on site companions with multistage task parallelization based on the MapReduce computing model. This method gives more efficient similar trajectory queries in a distributed computing environment for massive license plate recognition data. Tests show that this method can correctly query similar trajectories more efficiently than traditional stand-alone methods based on tests with almost ten million real vehicle license plate data points.

Keywords similar trajectory vehicle license plate recognition data site companion multistage task parallelization

ZTFLH:

TP319

Issue Date: 15 February 2017

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	ZHAO Zhuofeng
	LU Shuai
	HAN Yanbo

Cite this article:

ZHAO Zhuofeng,LU Shuai,HAN Yanbo. Similar trajectory query method based on massive vehicle license plate recognition data[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(2): 220-224.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.22.018 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I2/220

[1]	柴华骏, 李瑞敏, 郭敏. 基于车牌识别数据的城市道路旅行时间分布规律及估计方法研究[J]. 交通运输系统工程与信息, 2012, 12(6):41-47.CHAI Huajun, LI Ruimin, GUO Min. Travel time distribution and estimation of urban traffic using vehicle identification data[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(6):41-47. (in Chinese)
[2]	姜桂艳, 常安德, 牛世峰. 基于车牌识别数据的交通拥堵识别方法[J]. 哈尔滨工业大学学报, 2011, 43(4):131-135.JIANG Guiyan, CHANG Ande, NIU Shifeng. Traffic congestion identification method based on license plate recognition data[J]. Journal of Harbin Institute of Technology, 2011, 43(4):131-135. (in Chinese)
[3]	丁锐, 孟小峰, 杨楠. 一种高效的移动对象相似轨迹查询方法[J]. 计算机科学, 2003, 30(10):386-403.DING Rui, MENG Xiaofeng, YANG Nan. An efficient solution about similarity queries for moving object trajectories[J]. Computer Science, 2003, 30(10):386-403. (in Chinese)
[4]	Jensen C S, Lin D, Ooi B C. Continuous clustering of moving objects[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2007, 19(9):1161-1174.
[5]	Yang D, Rundensteiner E A, Ward M O. Neighbor-based pattern detection for windows over streaming data[C]//Proceedings of the 12th International Conference on Extending Database Technology (EDBT09). Saint Petersburg, Russia, 2009:529-540.
[6]	Jeung H, Yiu M, Zhou X. Discovery of convoys in trajectory databases[C]//Proceedings of the 36th International Conference on Very Large Data Bases (VLDB08). Auckland, New Zealand, 2008:1068-1080.
[7]	Xiong Y, Zhu Y. Mining peculiarity groups in day-by-day behavioral dataset[C]//Proceedings of the 9th International Conference on Data Mining (ICDM09). Miami, FL, USA, 2009:578-587.
[8]	Chang J, Song M, Um J. TMN-tree:New trajectory index structure for moving objects in spatial networks[C]//Proceedings of the 10th IEEE International Conference on Computer and Information Technology. Bradford, UK, 2010:1633-1638.
[9]	赵新勇, 安实. 伴随车检测技术应用研究[J]. 交通运输系统工程与信息, 2012, 12(3):36-40.ZHAO Xinyong, AN Shi. Research on accompanying cars recognition in practical application[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(3):36-40. (in Chinese)
[10]	Tang L, Zheng Y, Yuan J, et al. On discovery of traveling companions from streaming trajectories[C]//Proceedings of the 28th IEEE International Conference on Data Engineering (ICDE12). Arlington, VI, USA, 2012:186-197.
[11]	Tang L, Zheng Y, Yuan J, et al. A framework of traveling companion discovery on trajectory data streams[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1):3-1-3-34.
[12]	Zheng K, Zheng Y, Yuan J, et al. Online discovery of gathering patterns over trajectories[J]. IEEE Transactions on Data and Knowledge Engineering, 2014, 26(8):1-14.
[13]	Ekanayake J, Li H, Zhang B, et al. Twister:A runtime for iterative MapReduce[C]//Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. Chicago, IL, USA, 2010:810-818.
[14]	赵卓峰,丁维龙,韩燕波. 基于云架构的交通感知数据集成处理平台[J]. 计算机研究与发展, 2016, 53(6):1332-1341.ZHAO Zhuofeng, DING Weilong, HAN Yanbo. An intergrated processing platform for traffic sensor data based on cloud architecture[J]. Journal of Computer Research and Development, 2016, 53(6):1332-1341. (in Chinese)