Mining travel behaviors of tourists with mobile phone data: A case study in Hainan

2016 ◽  
Author(s):  
Feng Ling ◽  
Tianyue Sun ◽  
Xinning Zhu ◽  
Qingqing Chen ◽  
Xiaosheng Tang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Travel Behaviors

Assessing Refugees’ Onward Mobility with Mobile Phone Data—A Case Study of (Syrian) Refugees in Turkey

2019 ◽  
pp. 251-263
Author(s):  
Harald Sterly ◽  
Benjamin Etzold ◽  
Lars Wirkus ◽  
Patrick Sakdapolrak ◽  
Jacob Schewe ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Syrian Refugees

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

scholarly journals Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

2021 ◽  
Author(s):  
Alex A Berke ◽  
Ronan Doorley ◽  
Luis Alonso ◽  
Marc Pons ◽  
Vanesa Arroyo ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Compartmental Models ◽  
Mobile Phone Data ◽  
Population Changes ◽  
Study Region ◽  
Testing Program ◽  
Dynamic Population ◽  
Serology Testing

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

Urban spatiotemporal analysis using mobile phone data: Case study of medium- and large-sized Korean cities

2018 ◽  
Vol 73 ◽  
pp. 6-15 ◽  
Author(s):  
Kwang-Sub Lee ◽  
So Young You ◽  
Jin Ki Eom ◽  
Jiyoung Song ◽  
Jae Hong Min
Keyword(s):  
Mobile Phone ◽  
Spatiotemporal Analysis ◽  
Mobile Phone Data
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