An urban population flow analysis system based on mobile big data

Bao Ting; Zhang Zhi-gang; Jin Che-qing

doi:10.3969/j.issn.1000-5641.2015.05.014

Issue 5

Oct. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of East China Normal University (Natural Sciences) > 2015 > (5): 162-171

Bao Ting, Zhang Zhi-gang, Jin Che-qing. An urban population flow analysis system based on mobile big data[J]. Journal of East China Normal University (Natural Sciences), 2015, (5): 162-171. doi: 10.3969/j.issn.1000-5641.2015.05.014

Citation:

Bao Ting, Zhang Zhi-gang, Jin Che-qing. An urban population flow analysis system based on mobile big data[J]. Journal of East China Normal University (Natural Sciences), 2015, (5): 162-171. doi: 10.3969/j.issn.1000-5641.2015.05.014

Citation:

PDF( 2567 KB)

An urban population flow analysis system based on mobile big data

doi: 10.3969/j.issn.1000-5641.2015.05.014

Received Date: 2015-09-16
Publish Date: 2015-09-25

Abstract

Abstract

Analysis on urban population flow can help to make rational distribution of social resources, cope with traffic pressure and maintain public order, etc. The traditional manual analysis methods, such as questionnaire and interview, can not deal with this task efficiently. The continuous development and prevalence of smart phones bring great convenience to peoples daily life and users trajectory data generated by the connection between smart phones and base stations, which makes it possible to implement this task. However, trajectory data is massive and has low quality, which brings great challenge to related work. We propose a distributed framework for population flow analysis by using multiple computing nodes, thus greatly enhancing efficiency and scalability. In this paper, we use the massive trajectory data to analyze the behavior of urban population flow. We model flowing behavior among cities and among innercity districts, and decide the work place and living place of each person. Compared with the traditional methods, our method is cheaper and more efficient.
- population flowtrajectory datadistributed framework,

FullText(HTML)

References(1)

References

[1]

［1］GONZALEZ M C, HIDALGO C A, BARABASI A L. Understanding individual human mobility patterns［J］. Nature, 2008, 453(7196): 779782.

［2］SONG C, QU Z, BLUMM N, et al. Limits of predictability in human mobility［J］. Science, 2010, 327(5968): 10181021. 

［3］SONG C, KOREN T, WANG P, et al. Modelling the scaling properties of human mobility［J］. Nature Physics, 2010, 6(10): 818823.

［4］LI Z, DING B, HAN J, et al. Mining periodic behaviors for moving objects［C］ Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2010: 10991108.

［5］陈佳, 胡波, 左小清,等. 利用手机定位数据的用户特征挖掘［J］. 武汉大学学报：信息科学版, 2014, 39(6): 734738.

［6］ASHBROOK D, STARNER T. Using GPS to learn significant locations and predict movement across multiple users［J］. Personal and Ubiquitous Computing, 2003, 7(5): 275286.

［7］WHITE J, WELLS I. Extracting origin destination information from mobile phone data［C］11th International Conference on Road Transport Information and Control, 2002: 3034.

［8］CACERES N, WIDEBERG J P, BENITEZ F G. Deriving origin destination data from a mobile phone network［J］. Intelligent Transport Systems, IET, 2007, 1(1): 1526.

［9］IQBAL M S, CHOUDHURY C F, WANG P, et al. Development of origindestination matrices using mobile phone call data［J］. Transportation Research Part C Emerging Technologies, 2014, 40(1): 6374.

［10］LIU F, JANSSENS D, CUI J X, et al. Building a validation measure for activitybased transportation models based on mobile phone data［J］. Expert Systems with Applications, 2014, 41(14): 61746189.

［11］PHITHAKKITNUKOON S, HORANONT T, LORENZO G D, et al. Activityaware map: identifying human daily activity pattern using mobile phone data［C］ Proceedings of the First international conference on Human behavior understanding. SpringerVerlag, 2010:1425.

［12］ISAACMAN S, BECKER R, CACERES R, et al. Identifying Important Places in People′s Lives from Cellular Network Data［J］. Lecture Notes in Computer Science, 2011，6696: 133151.

［13］TRAAG V A, BROWET A, CALABRESE F, et al. Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference［C］ Proceedings of the Third IEEE International Conference on Social Computing, 2011: 911.

［14］QUERCIA D, LATHIA N, CALABRESE F, et al. Recommending social events from mobile phone location data［C］Proceedings of the 10th International Conference on Data Mining (ICDM), 2010: 971976.

［15］CALABRESE F, COLONNA M, LOVISOLO P, et al. RealTime Urban Monitoring Using Cell Phones: A Case Study In Rome［J］.IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 141151.

［16］SOTO V, FRIASMARTINEZ V, VIRSEDA J, et al. Prediction of Socioeconomic Levels Using Cell Phone Records［J］. Lecture Notes in Computer Science, 2011，6787: 377388.

［17］HONGYAN G, FASHENG L. Estimating freeway traffic measures from mobile phone location data［J］. European Journal of Operational Research, 2013, 229(1): 252260.

［18］陆嘉恒. Hadoop实战［M］.第2版.北京:机械工业出版社， 2012: 85329.

［19］孔扬鑫.手机轨迹数据的人口流动分析［R］.上海：华东师范大学软件工程学院,2015.

［18］章志刚.面向海量手机轨迹数据的重要位置发现［R］.上海：华东师范大学软件工程学院，2015.

Relative Articles

Supplements(0)

Cited By

Proportional views