Zhu, X.; Sun, T.; Yuan, H.; Hu, Z.; Miao, J. Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan. ISPRS Int. J. Geo-Inf.2019, 8, 74.
Zhu, X.; Sun, T.; Yuan, H.; Hu, Z.; Miao, J. Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan. ISPRS Int. J. Geo-Inf. 2019, 8, 74.
Zhu, X.; Sun, T.; Yuan, H.; Hu, Z.; Miao, J. Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan. ISPRS Int. J. Geo-Inf.2019, 8, 74.
Zhu, X.; Sun, T.; Yuan, H.; Hu, Z.; Miao, J. Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan. ISPRS Int. J. Geo-Inf. 2019, 8, 74.
Abstract
Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.
Keywords
Low accuracy CDRs; Group movement pattern; Data mining; Travel behaviors
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
