Analysis of Spatial-Temporal Characteristics Based on Mobile Phone Data

被引:0
|
作者
Yin, Hong-liang [1 ]
Zheng, Chang-jiang [2 ]
机构
[1] Jiangsu Prov Dept Commun, Nanjing 210098, Jiangsu, Peoples R China
[2] Hohai Univ, Sch Civil Engn & Transportat, Nanjing 210001, Jiangsu, Peoples R China
来源
关键词
Spatial-temporal characteristics; Mobile phone data; Urban transportation planning;
D O I
10.1007/978-981-10-3551-7_79
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The most traditional method to collect traffic data is household survey, which is a waste of manpower and material resources. OD matrices estimation using link volumes has been widely studied. But the significant shortcoming is the high cost of detectors. Besides, once installed in the network, the traffic detectors are not easy to be moved. Mobile phones' location data, however, can be acquired over a wider coverage without additional costs. The use of such data provides new spatiotemporal tools for improving urban transportation planning. This paper analyzes the nature and the pre-treatment of data from mobile phone operators in China and highlights the applicability of the data in domain of transportation. It also presents a typology of applications to analyze spatial-temporal characteristics based on mobile phone data.
引用
收藏
页码:989 / 998
页数:10
相关论文
共 50 条
  • [41] NOISE REDUCTION IN MODIS NDVI TIME SERIES DATA BASED ON SPATIAL-TEMPORAL ANALYSIS
    de Oliveira, Julio Cesar
    Neves Epiphanio, Jose Carlos
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 2372 - 2375
  • [42] Impact Analysis of transit travel spatial-temporal behavior based on Multiple Source data
    Chen Jun
    Tian Chao-jun
    Li Xiao-wei
    Fan Jing-kun
    SIXTH INTERNATIONAL CONFERENCE ON ELECTROMECHANICAL CONTROL TECHNOLOGY AND TRANSPORTATION (ICECTT 2021), 2022, 12081
  • [43] Spatial-Temporal Analysis of Field Evapotranspiration Based on Complementary Relationship Model and IKONOS Data
    Yang Guijun
    Zhao Chunjiang
    Xu Qingyun
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 2836 - 2839
  • [44] Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data
    Zhao, Kaiyu
    Fang, Zihang
    Li, Jingwei
    He, Chunyang
    RESOURCES CONSERVATION AND RECYCLING, 2023, 197
  • [45] Spatial-temporal analysis of carbon emissions from ships in ports based on AIS data
    Qi, Yuhao
    Yang, Jiaxuan
    Qin, Ken Sinkou
    OCEAN ENGINEERING, 2024, 308
  • [46] An analysis of agglomeration structure for Beijing, Tianjin, and Hebei based on spatial-temporal big data
    Tian, Ying
    Kan, Changcheng
    Li, Xiangyu
    Dang, Anrong
    COMPUTATIONAL URBAN SCIENCE, 2024, 4 (01):
  • [47] STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data
    Shi, Mingguang
    Cheng, Xudong
    Dai, Yulong
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2024, 112
  • [48] Spatio-Temporal Analysis of Mobile Phone Data for Interaction Recognition
    Ghahramani, Mohammadhossein
    Zhou, MengChu
    Hon, Chi Tin
    2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), 2018,
  • [49] Spatial Distribution Characteristics of People with Small Activity Space in Urban based on Mobile Phone Signaling Data
    Zhang X.
    Wu S.
    Zhao Z.
    Wang P.
    Chen Z.
    Fang Z.
    Journal of Geo-Information Science, 2021, 23 (08) : 1433 - 1445
  • [50] Modelling spatial and spatial-temporal data: a Bayesian approach
    Shanmugam, Ramalingam
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2021, 91 (07) : 1480 - 1481