Name
Abstract | ||
|---|---|---|
With the rapid growth of cell phone networks during the last decades, call detail records (CDR) have been used as approximate indicators for large scale studies on human and urban mobility. Although coarse and limited, CDR are a real marker of human presence. In this paper, we use more than 800 million CDR to identify weekly patterns of human mobility through mobile phone data. Our methodology is based on the classification of individuals into six distinct presence profiles where we focus on the inherent temporal and geographical characteristics of each profile within a territory. Then, we use an event-based algorithm to cluster individuals and we identify 12 weekly patterns. We leverage these results to analyze population estimates adjustment processes and as a result, we propose new indicators to characterize the dynamics of a territory. Our model has been applied to real data coming from more than 1.6 million individuals and demonstrates its relevance. The product of our work can be used by local authorities for human mobility analysis and urban planning. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMC.2017.2742953 | IEEE Trans. Mob. Comput. |
Keywords | Field | DocType |
Mobile computing,Mobile handsets,Data models,Cellular networks,Roads | Mobile computing,Data modeling,Population,Data mining,Computer science,Urban planning,Phone,Cellular network,Mobile phone,Cluster analysis,Distributed computing | Journal |
Volume | Issue | ISSN |
17 | 4 | 1536-1233 |
Citations | PageRank | References |
10 | 0.53 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thuillier, E. | 1 | 16 | 2.62 |
| Laurent Moalic | 2 | 30 | 7.19 |
| Sid Lamrous | 3 | 30 | 3.99 |
| Alexandre Caminada | 4 | 107 | 23.61 |