DoCoMo uses mobile network data to calculate population flows

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NTT DoCoMo announced that it has successfully developed population flow statistics for analyzing population mobility based on operational data from its nationwide mobile network in Japan.

The work is the result of research that DoCoMo conducted jointly with the National Institute for Land and Infrastructure Management (NILIM), a research arm of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), and the University of Tokyo’s Center for Spatial Information Science (CSIS) between July 2014 and September 2017.

A statistically reliable dataset was produced by combining the NILIM’s knowledge of urban-traffic research and planning, the University of Tokyo’s academic expertise in big-data utilization, and DoCoMo’s mobile communications and statistical processing technologies. The resulting population flow statistics offer the potential for use in diverse applications, including travel surveys such as ‘person trip’ surveys.

The research involved the statistical processing of operational data on mobile phones moving from one base station to another in DoCoMo’s network. The statistics were generated through a three-step process consisting of anonymization, estimation, and disclosure limitation. The resulting big data was then used to analyze how people moved about on a constant 24/7 basis throughout Japan.

From this dataset, it became possible to estimate population flows in given areas, as well as specific routes, distances and speeds traveled. The data also made it possible to identify transportation modes, such as air, high-speed rail (for bullet trains) and road (expressway) transport.

The research results have been released publicly in NILIM reports, and the NILIM reported the results at a session on June 11.

Examples of population flow statistics
Examples of population flow statistics

Going forward, DoCoMo expects the statistics to be used by various agencies and companies for highly effective and precise planning. For example, population flow statistics broken down by specific days, weeks, months and years could support the planning of efficient traffic networks, both urban and rural. Also, data on the number of people traveling between specific regions, including by age group and means of transportation, will facilitate the planning of networks tailored to Japan’s aging society and expected spikes in tourism.

In addition, DoCoMo says, the data could be used to plan appropriate increases in trains, buses and taxis to popular tourist spots and to help revitalize areas by devising more effective traffic signage.

The population flow statistics could also play an invaluable role in planning for the expected increase in visitors to Tokyo in 2020 for the Olympic Games. The data could be used to plan the efficient operation of rail and roadway networks during rush hours to accommodate both tourists and commuters, the operation of transport to/from sporting facilities and the development of necessary commercial facilities in and around event areas.

 

Examples of mobile spatial statistics population flow
Examples of mobile spatial statistics

 

Examples of population flow statistics output
Examples of population flow statistics output
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