Please wait a minute...
浙江大学学报(工学版)
人机交互与普适计算     
基于双层多粒度知识发现的移动轨迹预测模型
王亮, 於志文, 郭斌
1. 西安科技大学 电气与控制工程学院, 陕西 西安 710054; 
2. 西北工业大学 计算机学院, 陕西 西安 710072
Moving trajectory prediction model based on double layer multi-granularity knowledge discovery
WANG Liang, YU Zhi-wen, GUO Bin
1. School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China;
2. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
 全文: PDF(1600 KB)   HTML
摘要:

针对现实移动轨迹空间分布上的稀疏性、不均匀性对移动轨迹建模与预测方面带来的挑战性问题,提出基于双层多粒度移动轨迹知识发现的在线轨迹预测模型.分别在粗/细粒度语义层对移动轨迹数据进行多粒度建模与模式挖掘,基于粗/细粒度语义表达之间的关联包含关系建立双层移动模式映射索引结构.通过在双层语义模式上对在线移动轨迹进行匹配与输出合并互补操作,实现对在线查询移动轨迹的混合预测.在真实数据集上的实验结果表明,采用提出的方法能够有效地提升预测精度,可以扩展稀疏数据下的可预测轨迹范围.

Abstract:

An online trajectory prediction model was proposed based on multi-granularity knowledge discovery from double layers in view of the fact that the sparsity and heterogeneity in spatial distribution of reallife moving trajectory pose a challenge to model and predict moving trajectory. The operation of multi-granularity modeling and pattern mining were conducted for existing moving trajectory data on coarse/fine grained semantic layers, respectively. A hybrid prediction of online query moving trajectory can be achieved by leveraging the manipulation of matching and output complementary of online moving trajectory based on bi-layer semantic patterns. The experimental results on real data sets show that the proposed method can effectively improve the prediction accuracy and extend the range of predictable trajectory for sparse data.

出版日期: 2017-04-25
CLC:  TP 391  
基金资助:

国家“973”重点基础研究发展规划资助项目(2015CB352400);国家自然科学基金资助项目(61402360, 61373119);陕西省教育厅科学研究计划资助项目(16JK1509).

通讯作者: 於志文,男,教授,博导. ORCID:0000-0002-5023-5508.      E-mail: zhiwenyu@nwpu.edu.cn
作者简介: 王亮(1984—),男,讲师,博士后,从事普适计算、群智感知的研究. ORCID: 0000-0002-5897-4401. E-mail: liangwang0123@gmail.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

王亮, 於志文, 郭斌. 基于双层多粒度知识发现的移动轨迹预测模型[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.04.005.

WANG Liang, YU Zhi-wen, GUO Bin. Moving trajectory prediction model based on double layer multi-granularity knowledge discovery. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2017.04.005.

[1] DEGUCHI Y, KURODA K, SHOUJI M, et al. HEV charge/discharge control system based on navigation information [R]. Yokohama: Nissan Motor, 2004.
[2] KARBASSI A, BARTH M. Vehicle route prediction and time of arrival estimation techniques for improved transportation system management [C] ∥ Proceedings of Intelligent Vehicles Symposium. Columbus: IEEE, 2003: 511-516.
[3] FROEHLICH J, KRUMM J. Route prediction from trip observations [R]. Seattle: University of Washington, 2008.
[4] CHEN C, ZHANG D, MA X, et al. Crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis [J]. IEEE Transactions on Intelligent Transportation Systems, 2016(99):1-19.
[5] DAI J, YANG B, GUO C, et al. Personalized route recommendation using big trajectory data [C]∥2015 IEEE 31st International Conference on Data Engineering. Seoul: IEEE, 2015: 543-554.
[6] ZHANG S, QIN L, ZHENG Y, et al. Effective and efficient: large-scale dynamic city express [J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3203-3217.
[7] CHEN L, LV M, CHEN G. A system for destination and future route prediction based on trajectory mining [J]. Pervasive and Mobile Computing, 2010, 6(6):657-676.
[8] CHEN L, LV M, YE Q, WOODWARD J. A personal route prediction system based on trajectory data mining [J]. Information Sciences, 2011, 181(7): 1264-1284.
[9] TIWARI V S, ARYA A, CHATURVEDI S. Route prediction using trip observations and map matching [C] ∥2013 IEEE 3rd International Advance Computing Conference (IACC). Ghaziabad: IEEE, 2013: 583-587.
[10] SIMMONS R, BROWNING B, ZHANG Y, et al. Learning to predict driver route and destination intent [C] ∥ IEEE Intelligent Transportation Systems Conference. Toronto: IEEE, 2006: 127-132.
[11] MONREALE A, PINELLI F, TRASARTI R, et al. Wherenext: a location predictor on trajectory pattern mining [C] ∥ Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris: ACM, 2009: 637-646.
[12] XUE A Y, ZHANG R, ZHENG Y, et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction [C] ∥ 2013 IEEE 29th International Conference on Data Engineering (ICDE). Brisbane: IEEE, 2013: 254-265.
[13] XUE A Y, QI J, XIE X, et al. Solving the data sparsity problem in destination prediction [J]. The VLDB Journal, 2015, 24(2): 219-243.
[1] 郑守国,张勇德,谢文添,樊虎,王青. 基于数字孪生的飞机总装生产线建模[J]. 浙江大学学报(工学版), 2021, 55(5): 843-854.
[2] 张师林,马思明,顾子谦. 基于大边距度量学习的车辆再识别方法[J]. 浙江大学学报(工学版), 2021, 55(5): 948-956.
[3] 宋鹏,杨德东,李畅,郭畅. 整体特征通道识别的自适应孪生网络跟踪算法[J]. 浙江大学学报(工学版), 2021, 55(5): 966-975.
[4] 蔡君,赵罡,于勇,鲍强伟,戴晟. 基于点云和设计模型的仿真模型快速重构方法[J]. 浙江大学学报(工学版), 2021, 55(5): 905-916.
[5] 王虹力,郭斌,刘思聪,刘佳琪,仵允港,於志文. 边端融合的终端情境自适应深度感知模型[J]. 浙江大学学报(工学版), 2021, 55(4): 626-638.
[6] 张腾,蒋鑫龙,陈益强,陈前,米涛免,陈彪. 基于腕部姿态的帕金森病用药后开-关期检测[J]. 浙江大学学报(工学版), 2021, 55(4): 639-647.
[7] 郑英杰,吴松荣,韦若禹,涂振威,廖进,刘东. 基于目标图像FCM算法的地铁定位点匹配及误报排除方法[J]. 浙江大学学报(工学版), 2021, 55(3): 586-593.
[8] 雍子叶,郭继昌,李重仪. 融入注意力机制的弱监督水下图像增强算法[J]. 浙江大学学报(工学版), 2021, 55(3): 555-562.
[9] 于勇,薛静远,戴晟,鲍强伟,赵罡. 机加零件质量预测与工艺参数优化方法[J]. 浙江大学学报(工学版), 2021, 55(3): 441-447.
[10] 胡惠雅,盖绍彦,达飞鹏. 基于生成对抗网络的偏转人脸转正[J]. 浙江大学学报(工学版), 2021, 55(1): 116-123.
[11] 陈杨波,伊国栋,张树有. 基于点云特征对比的曲面翘曲变形检测方法[J]. 浙江大学学报(工学版), 2021, 55(1): 81-88.
[12] 段有康,陈小刚,桂剑,马斌,李顺芬,宋志棠. 基于相位划分的下肢连续运动预测[J]. 浙江大学学报(工学版), 2021, 55(1): 89-95.
[13] 张太恒,梅标,乔磊,杨浩杰,朱伟东. 纹理边界引导的复合材料圆孔检测方法[J]. 浙江大学学报(工学版), 2020, 54(12): 2294-2300.
[14] 梁栋,刘昕宇,潘家兴,孙涵,周文俊,金子俊一. 动态背景下基于自更新像素共现的前景分割[J]. 浙江大学学报(工学版), 2020, 54(12): 2405-2413.
[15] 晋耀,张为. 采用Anchor-Free网络结构的实时火灾检测算法[J]. 浙江大学学报(工学版), 2020, 54(12): 2430-2436.