Shi et al., 2017 - Google Patents
Mobility patterns analysis of Beijing residents based on call detail recordsShi et al., 2017
- Document ID
- 13706157686862286009
- Author
- Shi L
- Wang W
- Cai W
- Wang Z
- Zhang S
- Zhou W
- Publication year
- Publication venue
- 2017 9th International conference on wireless communications and signal processing (WCSP)
External Links
Snippet
Human mobility pattern analysis are interesting and important topic which has become feasible nowadays by the flashing development of data collection and analysis platform technology. This paper leverages Call Detail Records (CDRs) data to analyze the mobility …
- 230000002354 daily 0 abstract description 16
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
- H04W4/02—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
- H04W4/023—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
- H04W4/02—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
- H04W4/025—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS using location based information parameters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xu et al. | Understanding mobile traffic patterns of large scale cellular towers in urban environment | |
Wang et al. | Spatio-temporal analysis and prediction of cellular traffic in metropolis | |
Qiao et al. | A mobility analytical framework for big mobile data in densely populated area | |
Zhao et al. | Understanding the bias of call detail records in human mobility research | |
Pappalardo et al. | Using big data to study the link between human mobility and socio-economic development | |
Isaacman et al. | Identifying important places in people’s lives from cellular network data | |
KR20190139130A (en) | Analysis method of fluidized population information capable of providing real-time fluidized population data by pcell algorithm | |
US20130166352A1 (en) | Mobile categorization | |
Chen et al. | Enriching sparse mobility information in call detail records | |
Doyle et al. | Population mobility dynamics estimated from mobile telephony data | |
Demissie et al. | Understanding human mobility patterns in a developing country using mobile phone data | |
Furletti et al. | Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach | |
Kuber et al. | Traffic prediction by augmenting cellular data with non-cellular attributes | |
CN110322067A (en) | Location of mobile users prediction technique based on factor graph model | |
Fekih et al. | Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data | |
Li et al. | Mobile user location prediction based on user classification and markov model | |
Xiong et al. | Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data | |
Jiang et al. | Crowd flow prediction for social internet-of-things systems based on the mobile network big data | |
CN112399458A (en) | Big data analysis method for mobile communication network flow | |
Shi et al. | Mobility patterns analysis of Beijing residents based on call detail records | |
CN110992230B (en) | Full-scale demographic method, device and server based on terminal signaling data | |
Sun et al. | Characterizing user mobility from the view of 4G cellular network | |
Akin et al. | Estimating origin-destination matrices using location information from cellular phones | |
Lima et al. | Human Mobility Support for Personalized Data Offloading | |
Zhao et al. | User mobility modeling based on mobile traffic data collected in real cellular networks |