Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach

Yang Xu (), Shih-Lung Shaw (), Ziliang Zhao (), Ling Yin (), Zhixiang Fang () and Qingquan Li ()

Transportation, 2015, vol. 42, issue 4, 625-646

Abstract: Advancements of information, communication and location-aware technologies have made collections of various passively generated datasets possible. These datasets provide new opportunities to understand human mobility patterns at a low cost and large scale. This study presents a home-based approach to understanding human mobility patterns based on a large mobile phone location dataset from Shenzhen, China. First, we estimate each individual’s “home” anchor point, and a modified standard distance ( $$S_{D}^{\prime }$$ S D ′ ) is proposed to measure the spread of each individual’s activity space centered at this “home” anchor point. We then derive aggregate mobility patterns at mobile phone tower level to describe the distance distribution of $$S_{D}^{\prime }$$ S D ′ for people who share the same “home” anchor point. A hierarchical clustering algorithm is performed and the spatial distributions of the derived clusters are analyzed to highlight areas with similar aggregate human mobility patterns. The results suggest that 43 % of the population sample travelled within a short distance ( $$S_{D}^{\prime } \le 1 \;{\text{km}}$$ S D ′ ≤ 1 km ) during the 13-day study period while 23.9 % of them were associated with a large activity space ( $$S_{D}^{\prime } \ge 5 \;{\text{km}}$$ S D ′ ≥ 5 km ). The geographical differences of people’s mobility patterns in Shenzhen are evident. Areas with a large proportion of people who have a small activity space mainly locate in the northern part of Shenzhen such as Baoan and Longgang districts. In the southern part where the economy is highly developed, the percentage of people with a larger activity space is higher in general. The findings could offer useful implications on policy and decision making. The proposed approach can also be used in other studies involving similar spatiotemporal datasets for travel behavior and policy analysis. Copyright Springer Science+Business Media New York 2015

Keywords: Passive mobile phone location data; Human mobility; Activity space; Home-based approach (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11116-015-9597-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:42:y:2015:i:4:p:625-646

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11116/PS2

DOI: 10.1007/s11116-015-9597-y

Access Statistics for this article

Transportation is currently edited by Kay W. Axhausen

More articles in Transportation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().