基于稀疏轨迹聚类的自驾车旅游路线挖掘

doi:10.11772/j.issn.1001-9081.2019081467

计算机应用 ›› 2020, Vol. 40 ›› Issue (4): 1079-1084.DOI: 10.11772/j.issn.1001-9081.2019081467

基于稀疏轨迹聚类的自驾车旅游路线挖掘

杨奉毅^1,2,3, 马玉鹏^1,3, 包恒彬^1,2,3, 韩云飞^1,3, 马博^1,2,3

1. 中国科学院新疆理化技术研究所, 乌鲁木齐 830011;
2. 中国科学院大学, 北京 100049;
3. 新疆民族语音语言信息处理实验室, 乌鲁木齐 830011

收稿日期:2019-08-23 修回日期:2019-10-19 出版日期:2020-04-10 发布日期:2019-11-18
通讯作者: 马玉鹏
作者简介:杨奉毅(1994-),男,山东济宁人,硕士研究生,主要研究方向:大数据分析、数据挖掘;马玉鹏(1979-),男,新疆阜康人,研究员,博士,CCF会员,主要研究方向:物联网、大数据分析;包恒彬(1995-),男,辽宁本溪人,硕士研究生,主要研究方向:大数据分析、数据挖掘;韩云飞(1990-),男,山西晋城人,助理研究员,博士,主要研究方向:数据挖掘、计算机视觉;马博(1984-),男,辽宁鞍山人,副研究员,博士,CCF会员,主要研究方向:大数据分析、知识图谱。
基金资助:
新疆维吾尔自治区自然科学基金资助项目（2019D01A92）；新疆天山杰出青年计划项目（2018Q005）。

Self-driving tour route mining based on sparse trajectory clustering

YANG Fengyi^1,2,3, MA Yupeng^1,3, BAO Hengbin^1,2,3, HAN Yunfei^1,3, MA Bo^1,2,3

1. The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi Xinjiang 830011, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi Xinjiang 830011, China

Received:2019-08-23 Revised:2019-10-19 Online:2020-04-10 Published:2019-11-18
Supported by:
This work is partially supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01A92),the Tianshan Distinguished Young Scholars of Xinjiang(2018Q005).

摘要/Abstract

摘要： 针对自驾车游客加油轨迹稀疏，还原真实旅游路线困难的问题，提出一种基于语义表示的稀疏轨迹聚类算法，用以挖掘流行的自驾车旅游路线。与基于轨迹点匹配的传统轨迹聚类算法不同，该算法考虑不同轨迹点之间的语义关系，学习轨迹的低维向量表示。首先，利用神经网络语言模型学习加油站点的分布式向量表示；然后，取每条轨迹中所有站点向量的平均值作为该轨迹的向量表示；最后，采用经典的k均值算法对轨迹向量进行聚类。最终的可视化结果表明，所提算法有效地挖掘出了两条流行的自驾车旅游线路。

关键词: 稀疏轨迹, 旅游路线挖掘, 轨迹聚类, 分布式表示, 自驾车旅游

Abstract: Aiming at the difficulty of constructing real tour routes from sparse refueling trajectories of self-driving tourists,a sparse trajectory clustering algorithm based on semantic representation was proposed to mine popular self-driving tour routes. Different from traditional trajectory clustering algorithms based on trajectory point matching,in this algorithm, the semantic relationships between different trajectory points were considered and the low-dimensional vector representation of the trajectory was learned. Firstly,the neural network language model was used to learn the distributed vector representation of the gas stations. Then,the average value of all the station vectors in each trajectory was taken as the vector representation of this trajectory. Finally,the classical k-means algorithm was used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm mines two popular self-driving tour routes effectively.

Key words: sparse trajectory, tour route mining, trajectory clustering, distributed representation, self-driving tour

中图分类号:

TP391.4

杨奉毅, 马玉鹏, 包恒彬, 韩云飞, 马博. 基于稀疏轨迹聚类的自驾车旅游路线挖掘[J]. 计算机应用, 2020, 40(4): 1079-1084.

YANG Fengyi, MA Yupeng, BAO Hengbin, HAN Yunfei, MA Bo. Self-driving tour route mining based on sparse trajectory clustering[J]. Journal of Computer Applications, 2020, 40(4): 1079-1084.

参考文献

[1] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 2013 Conference on Neural Information Processing Systems. Cambridge:MIT Press,2013:3111-3119.
[2] 常亮, 孙文平, 张伟涛, 等. 旅游路线规划研究综述[J]. 智能系统学报,2019,14(1):82-92.(CHANG L,SUN W P,ZHANG W T,et al. Review of tourism route planning[J]. CAAI Transactions on Intelligent Systems,2019,14(1):82-92.)
[3] ZHENG Y,ZHANG L,MA Z,et al. Recommending friends and locations based on individual location history[J]. ACM Transactions on the Web,2011,5(1):No. 5.
[4] ZHENG Y,ZHANG L,XIE X,et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th International Conference on World Wide Web. New York:ACM,2009:791-800.
[5] ZHENG V W,ZHENG Y,XIE X,et al. Collaborative location and activity recommendations with GPS history data[C]//Proceedings of the 19th International Conference on World Wide Web. New York:ACM,2010:1029-1038.
[6] CUI G,LUO J,WANG X. Personalized travel route recommendation using collaborative filtering based on GPS trajectories[J]. International Journal of Digital Earth,2018,11(3):284-307.
[7] JIANG S,QIAN X,SHEN J,et al. Author topic model-based collaborative filtering for personalized POI recommendations[J]. IEEE Transactions on Multimedia,2015,17(6):907-918.
[8] WEI L Y,ZHENG Y,PENG W C. Constructing popular routes from uncertain trajectories[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2012:195-203.
[9] 刘艳平, 保继刚, 黄应淮, 等. 基于GPS数据的自驾车游客时空行为研究——以西藏为例[J]. 世界地理研究,2019,28(1):149-160. (LIU Y P, BAO J G, HUANG Y H, et al. Study on spatio-temporal behaviors of self-driving tourists based on GPS data:a case study of Tibet[J]. World Regional Studies,2019,28(1):149-160.)
[10] ZHENG Y. Trajectory data mining:an overview[J]. ACM Transactions on Intelligent Systems and Technology,2015,6(3):No. 29.
[11] LEE J G, HAN J, WHANG K Y. Trajectory clustering:a partition-and-group framework[C]//Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. New York:ACM,2007:593-604.
[12] TANG W,PI D,HE Y. A density-based clustering algorithm with sampling for travel behavior analysis[C]//Proceedings of the 2016 International Conference on Intelligent Data Engineering and Automated Learning,LNCS 9937. Cham:Springer,2016:231-239.
[13] BESSE P C,GUILLOUET B,LOUBES J M,et al. Review and perspective for distance-based clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems, 2016,17(11):3306-3317.
[14] YI B K,JAGADISH H V,FALOUTSOS C. Efficient retrieval of similar time sequences under time warping[C]//Proceedings of the 14th International Conference on Data Engineering. Piscataway:IEEE,1998:201-208.
[15] VLACHOS M,KOLLIOS G,GUNOPOULOS D. Discovering similar multidimensional trajectories[C]//Proceedings of the 18th International Conference on Data Engineering. Piscataway:IEEE, 2002:673-684.
[16] CHEN L,ÖZSU M T,ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York:ACM,2005:491-502.
[17] WANG J,HUANG P,ZHAO H,et al. Billion-scale commodity embedding for e-commerce recommendation in Alibaba[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:839-848.
[18] KENTHAPADI K,LE B,VENKATARAMAN G. Personalized job recommendation system at LinkedIn:practical challenges and lessons learned[C]//Proceedings of the 11th ACM Conference on Recommender Systems. New York:ACM,2017:346-347.
[19] RADOSAVLJEVIC V,GRBOVIC M,DJURIC N,et al. Smartphone app categorization for interest targeting in advertising marketplace[C]//Proceedings of the 25th International Conference Companion on World Wide Web. New York:ACM,2016:93-94.
[20] GRBOVIC M, CHENG H. Real-time personalization using embeddings for search ranking at Airbnb[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:311-320.
[21] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2014:701-710.
[22] WIETING J,BANSAL M,GIMPEL K,et al. Towards universal paraphrastic sentence embeddings[EB/OL].[2019-09-20]. https://arxiv.org/pdf/1511.08198.pdf.

基于稀疏轨迹聚类的自驾车旅游路线挖掘

Self-driving tour route mining based on sparse trajectory clustering

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

[1]	潘一荣, 李晓, 杨雅婷, 米成刚, 董瑞. 面向汉维机器翻译的调序表重构模型[J]. 计算机应用, 2018, 38(5): 1283-1288.
[2]	曲昭伟, 王源, 王晓茹. 基于迁移学习的分层注意力网络情感分析算法[J]. 计算机应用, 2018, 38(11): 3053-3056.