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

By Shan Jiang, Joseph Ferreira Jr., Marta C. González. 2017

435 citations recorded by the source corpus.

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.

Subfields: Transportation

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “…and understanding human mobility include distance and duration distributions [191], origin-destination matrices [103], individual activity-based mobility patterns [192], transportation mode identiﬁcation [54], and densities and ﬂows within a building (or a cluster of buildings) [138], [193].”
Understanding Urban Human Mobility through Crowdsensed Data (2018, methodology): “M.6 - Clustering: In mobility research, clustering is mainly used to: 1) ﬁnd people’s staying spots and PoIs [4], 2) cluster people into different groups by their mobility patterns [13], 3) cluster cell stations (or BDRs and WFPRRs sensing stations) into different groups by their utility patterns…”
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “In general, paratransit services in developing countries exhibit the following characteristics: operate without scheduled timetable and are not restricted in terms of the routes or areas in which they may operate; the vehicles are typically small, ranging from 4 seat sedan to midibuses with 17–35…”