南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (6): 1171–1182.doi: 10.13232/j.cnki.jnju.2018.06.013

• • 上一篇    下一篇

具有时空特性的区域移动模式挖掘算法

周星星1,2,3，张海平2,3，吉根林1,2,3*   

  1. 1.南京师范大学计算机科学与技术学院，南京，210023；2.南京师范大学地理科学学院，南京，210023； 3．江苏省地理信息资源开发与利用协同创新中心，南京，210023
  • 接受日期:2018-09-10 出版日期:2018-12-01 发布日期:2018-12-01
  • 通讯作者: 吉根林, glji@njnu.edu.cn E-mail:glji@njnu.edu.cn
  • 基金资助:
    国家自然科学基金(41471371)

An algorithm for mining movement patterns between zones with spatio－temporal characteristics

Zhou Xingxing1,2,3，Zhang Haiping2,3，Ji Genlin1,2,3*   

  1. 1.School of Computer Science and Technology，Nanjing Normal University，Nanjing，210023，China； 2．School of Geographic Science，Nanjing Normal University；Nanjing，210023，China； 3．Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application，Nanjing，210023，China
  • Accepted:2018-09-10 Online:2018-12-01 Published:2018-12-01
  • Contact: Ji Genlin, glji@njnu.edu.cn E-mail:glji@njnu.edu.cn

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摘要： 丰富的居民出行行为信息对挖掘城市热点区域以及居民出行模式有很大的帮助，并且对更好地满足居民出行需求也有一定的启示作用. 最新的相关研究主要聚焦于城市中区域之间的空间移动模式，但并不能识别移动模式发生的时间以及持续的时长. 针对这一问题，提出具有时空特性的区域移动模式挖掘算法STMPZ(Spatio－Temporal based Movement Patterns between Zones). 该算法在DBSCAN(Density－based Spatial Clustering of Applications with Noise)算法的基础上，通过将对象从点扩展成一条出行OD(Origin－Destination)记录，并引入时间特性，最终可以挖掘出具有时空特性的区域移动模式. 为了验证所提出算法的可行性和有效性，利用真实的上海地铁通勤数据集进行实验，实验结果表明，该算法可以快速有效地检测出具有高覆盖率和准确率的区域移动模式. 此外，该算法也可以通过修改聚类过程的参数应用于其他区域或类型的交通数据．

关键词: 区域移动模式, 时空分析, 出行行为, 移动模式挖掘

Abstract: The extensive information of residents’travel behavior is very helpful for mining hotspots in cities and the patterns of residents’travel. It also has a certain enlightening effect on better meeting residents’travel needs. The recent related researches mainly focus on the patterns of spatial movement among regions in cities，which cannot identify the occurrence time and duration of movement patterns. Based on the existing researches and problems，a new algorithm STMPZ(Spatio－Temporal based Movement Patterns between Zones)is presented for mining movement patterns between zones with spatiotemporal characteristics. Based on the DBSCAN(Density－Based Spatial Clustering of Applications with Noise)，the algorithm extends the point object to a trip record，and introduces time characteristic which can eventually mine the regional moving patterns with time and space characteristics. Because the STMPZ algorithm considers the spatial proximity and temporal proximity between objects in the process of regional mobile pattern mining，it does not only need to define the time interval，but also compensates for the traditional algorithm，even through the predefined time interval. It is also impossible to recognize the defect of the area movement pattern which occurs at any time and lasts for an arbitrary length of time. In order to illustrate the feasibility and effectiveness of the proposed algorithm，real subway data sets in Shanghai were used for experimentation. The results show that the algorithm can effectively mine movement patterns between areas with high coverage and accuracy. The algorithm can also be applied to other areas or types of traffic data by modifying the parameters of the clustering process.

Key words: movement pattern between areas, spatio－temporal analysis, travel behavior, movement pattern mining

中图分类号: 

  • TP301.6
[1]　Pan G，Qi G D，Wu Z H，et al. Land－use classification using taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems，2013，14(1)：113－123.
[2] Akabal F M，Masirin M I H M，Akasah Z A，et al. Review on selection and suitability of rail transit station design pertaining to public safety. IOP Conference Series：Materials Science and Engineering，2017，226(1)：012033.
[3] Blei D M，Ng A Y，Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research，2003，3(4－5)：993－1022.
[4] Hong L J，Davison B D. Empirical study of topic modeling in Twitter ∥ Proceedings of the 1st Workshop on Social Media Analytics. Washington DC，USA：ACM，2010：80－88.
[5] Pei T，Wang W Y，Zhang H C，et al. Density－based clustering for data containing two types of points. International Journal of Geographical Information Science，2015，29(2)：175－193.
[6] Aung H H，Tan K L. Discovery of evolving convoys ∥ Gertz M，Ludscher B. Scientific and statistical database management. Springer Berlin Heidelberg，2010：196－213.
[7] Wang Y D，Lim E P，Hwang S Y. Efficient algorithms for mining maximal valid groups. The VLDB Journal，2008，17(3)：515－535.
[8] Li Y X，Bailey J，Kulik L. Efficient mining of platoon patterns in trajectory databases. Data & Knowledge Engineering，2015，100：167－187.
[9] Williams H J，Holton M D，Shepard E L C，et al. Identification of animal movement patterns using tri－axial magnetometry. Movement Ecology，2017，5(1)：6.
[10] Andris C，Liu X，Ferreira J Jr. Challenges for social flows. Computers，Environment And Urban Systems，2018，70：197－207.
[11] Kempinska K，Longley P，Shawe－Taylor J. Interactional regions in cities：Making sense of flows across networked systems. International Journal of Geographical Information Science，2018，32(7)：1348－1367.
[12] Kim K，Oh K，Lee Y K，et al. An analysis on movement patterns between zones using smart card data in subway networks. International Journal of Geographical Information Science，2014，28(9)：1781－1801.
[13] Chen Z L，Gong X，Xie Z. An analysis of movement patterns between zones using taxi GPS data. Transactions in Gis，2017，21(6)：1341－1363.
[14] Zhu D，Huang Z，Shi L，et al. Inferring spatial interaction patterns from sequential snapshots of spatial distributions. International Journal of Geographical Information Science，2018，32(4)：783－805.
[15] Zhang H P，Zhou X X，Gu X，et al. Method for the analysis and visualization of similar flow hotspot patterns between different regional groups. ISPRS International Journal of Geo－Information，2018，7(8)：328.
[16] Ma X L，Wu Y J，Wang Y H，et al. Mining smart card data for transit riders’ travel patterns. Transportation Research Part C：Emerging Technologies，2013，36：1－12.
[17] Ma X L，Wang Y H，Chen F，et al. Transit smart card data mining for passenger origin information extraction. Journal of Zhejiang University－Science C，2012，13(10)：750－760.
[18] He B，Ding Y，Yan E J. Mining patterns of author orders in scientific publications. Journal of Informetrics，2012，6(3)：359－367.
[19] Chen Z Y，Liu Z Q. Effects of central column aspect ratio on seismic performances of subway station structures. Advances in Structural Engineering，2018，21(1)：14－29.
[20] Esmaeili－Falak M，Katebi H，Javadi A. Experimental study of the mechanical behavior of frozen soils － A case study of Tabriz subway. Periodica Polytechnica Civil Engineering，2018，62(1)：117－125.
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