Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data
A Case Study of Singapore
Bibliographic Data
| ID | 23319218 |
|---|---|
| Authors | Shan Jiang (0000-0001-7766-222X, Massachusetts Institute of Technology), Joseph Ferreira (0000-0003-0600-3803, Massachusetts Institute of Technology), M Christina Gonzalez (0000-0002-8482-0318, Massachusetts Institute of Technology) |
| Year | 2017 |
| Volume | 3 |
| Issue | 2 |
| Pages | 208-219 |
| Publication date | 2017-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Big Data (JOURNAL) |
| Journal identifiers | ISSN: 2332-7790 • E-ISSN: 2332-7790 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tbdata.2016.2631141 |
| OpenAlex | W2556289220 |
| Language | EN |
| Citations received | 75 |
| References cited | 43 |
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.
Data science · Human–computer interaction · Mobile phone · Phone · Pipeline (software) · Raw data · Telecommunications · Urban computing · Computer Science · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility
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| Unique citing works | 75 |
|---|---|
| Citations per year | 9,38 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 73 |