全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

城市手机用户移动轨迹时空熵特征分析
An Analysis of Entropy of Human Mobility from Mobile Phone Data

DOI: 10.13203/j.whugis20160203

Keywords: 手机话单,个体轨迹,活动提取,出行模式,时空熵,
call detailed records
,digital traces,displacement identification,mobility patterns,trajectory entropy

Full-Text   Cite this paper   Add to My Lib

Abstract:

利用手机话单数据分析城市个体居民移动活动的时间熵和空间熵特征,一方面探讨了从原始话单记录中进行出行识别的必要性,另一方面提出了一种考虑空间邻近性的轨迹近似熵特征分析方法。其中,出行识别可以克服手机定位数据采样频率较低的缺陷;近似熵分析方法具有强空间鲁棒性,可以减少因手机定位数据空间精度较低带来的影响。实证结果表明,城市居民出行活动既具有强烈的目的地选择倾向,同时也具有强烈的移动路径选择偏好

References

[1]  Cover T M, Thomas J A. Elements of Information Theory[M]. New York:Wiley, 1991
[2]  Sevtsuk A, Ratti C. Does Urban Mobility have a Daily Routine? Learning from the Aggregate Data of Mobile Phone Networks[J]. <em>Journal of Urban Technology</em>, 2010, 17(1):41-60
[3]  Ahas R, Aasa A, Roose A, et al. Evaluating Passive Mobile Positioning Data for Tourism Surveys:An Estonian Case Study[J]. <em>Tourism Management</em>, 2008, 29(3):469-486
[4]  Liu L, Andris C, Ratti C. Uncovering Cabdrivers' Behavior Patterns from Their Digital Traces[J]. <em>Computers, Environment and Urban Systems</em>, 2010, 34(6):541-548
[5]  Wesolowski A, Eagle N, Tatem A J, et al. Quantifying the Impact of Human Mobility on Malaria[J]. <em>Science</em>, 2012, 338(6104):267-270
[6]  Brockmann D, Hufnagel L, Geisel T. The Scaling Laws of Human Travel[J]. <em>Nature</em>, 2006, 439(7075):462-465
[7]  Pincus S M. Approximate Entropy as a Measure of System Complexity[J]. <em>The National Academy of Sciences</em>, 1991, 88(6):2297-2301
[8]  Liu Y, Liu X, Gao S, et al. Social Sensing:A New Approach to Understanding Our Socio-Economic Environments[J]. <em>Annals of the Association of American Geographers</em>, 2015, 105(3):512-530
[9]  Ratti C, Frenchman D, Pulselli R M, et al. Mobile Landscapes:Using Location Data from Cell Phones for Urban Analysis[J]. <em>Environment and Planning B-Planning & Design</em>, 2006, 33:727-748
[10]  Zhou Tao, Han Xiaopu, Yan Xiaoyong, et al. Statistical Mechanics on Temporal and Spatial Activities of Human[J]. <em>Journal of University of Electronic Science and Technology of China</em>, 2013, 42:481-540(周涛,韩筱璞,闫小勇,等. 人类行为时空特性的统计力学[J]. 电子科技大学学报, 2013, 42:481-540)
[11]  González M C, Hidalgo C A, Barabási A L. Understanding Individual Human Mobility Patterns[J]. <em>Nature</em>, 2008, 453:779-782
[12]  Song C, Qu Z, Blumm N, et al. Limits of Predictability in Human Mobility[J]. <em>Science</em>, 2010, 327(5968):1018-1021
[13]  Jiang S, Fiore G A, Yang Y, et al. A Review of Urban Computing for Mobile Phone Traces:Current Methods, Challenges and Opportunities[C]. The 2nd ACM SIGKDD International Workshop on Urban Computing, New York, 2013
[14]  Calabrese F, Lorenzo G D, Liu L, et al. Estimating Origin-Destination Flows Using Mobile Phone Location Data[J]. <em>IEEE Pervasive Computing</em>, 2011, 10(4):36-44
[15]  Schneider C M, Belik V, Couronné T, et al. Unravelling Daily Human Mobility Motifs[J]. <em>Journal of The Royal Society Interface</em>, 2013, DOI:10.1098/rsif.2013.0246
[16]  Eagle N, Pentland A S. Eigenbehaviors:Identifying Structure in Routine[J]. <em>Behavioral Ecology and Sociobiology</em>, 2009, 63(7):1057-1066

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133