Propagation Models and Analysis for Mobile Phone Data Analytics

2015 ◽  
pp. 257-292
Author(s):  
Derek Doran ◽  
Veena Mendiratta
Keyword(s):  
Mobile Phone ◽  
Data Analytics ◽  
Mobile Phone Data ◽  
Propagation Models

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

scholarly journals Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamid Khataee ◽  
Istvan Scheuring ◽  
Andras Czirok ◽  
Zoltan Neufeld
Keyword(s):  
Mobile Phone ◽  
Basic Reproductive Number ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Expansion Phase ◽  
Social Distancing ◽  
Exponential Expansion ◽  
Total Death ◽  
Mobility Reduction ◽  
Epidemiological Parameters

AbstractA better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

scholarly journals Did the NSA and GCHQ Diminish Our Privacy? What the Control Account Should Say

2020 ◽  
Vol 7 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Leonhard Menges
Keyword(s):  
Mobile Phone ◽  
Personal Information ◽  
Real Life ◽  
Mobile Phone Data ◽  
Standard Account ◽  
Do So

AbstractA standard account of privacy says that it is essentially a kind of control over personal information. Many privacy scholars have argued against this claim by relying on so-called threatened loss cases. In these cases, personal information about an agent is easily available to another person, but not accessed. Critics contend that control accounts have the implausible implication that the privacy of the relevant agent is diminished in threatened loss cases. Recently, threatened loss cases have become important because Edward Snowden’s revelation of how the NSA and GCHQ collected Internet and mobile phone data presents us with a gigantic, real-life threatened loss case. In this paper, I will defend the control account of privacy against the argument that is based on threatened loss cases. I will do so by developing a new version of the control account that implies that the agents’ privacy is not diminished in threatened loss cases.

Vehicle Travel Path Recognition in Urban Dense Road Network Environments by Using Mobile Phone Data

2021 ◽  
pp. 1-29
Author(s):  
Yudong Guo ◽  
Fei Yang ◽  
Peter Jing Jin ◽  
Haode Liu ◽  
Sai Ma ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Road Network ◽  
Mobile Phone Data ◽  
Travel Path ◽  
Network Environments

Analysis of urban agglomeration structure through spatial network and mobile phone data

2021 ◽  
Author(s):  
Xintao Liu ◽  
Jianwei Huang ◽  
Jianhui Lai ◽  
Junwei Zhang ◽  
Ahmad M. Senousi ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Agglomeration ◽  
Mobile Phone Data ◽  
Spatial Network

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

2017 ◽  
Vol 3 (2) ◽  
pp. 208-219 ◽  
Author(s):  
Shan Jiang ◽  
Joseph Ferreira ◽  
Marta C. Gonzalez
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Mobility Patterns

scholarly journals Quantifying the Impact of Accessibility on Preventive Healthcare in Sub-Saharan Africa Using Mobile Phone Data

Epidemiology ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 223-228 ◽  
Author(s):  
Amy Wesolowski ◽  
Wendy Prudhomme O’Meara ◽  
Andrew J. Tatem ◽  
Samson Ndege ◽  
Nathan Eagle ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Sub Saharan Africa ◽  
Mobile Phone Data ◽  
Preventive Healthcare ◽  
Sub Saharan ◽  
The Impact
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