Abstract
Monitoring and modelling crowd movement enables a plethora of applications.
Crowd-movement analysis has classically been done manually, only at large
scales (spatial and temporal) and based on small samples. By automating the
process, we can dramatically increase the sample size, the amount of data. WiFi
remote-positioning is currently the most popular technology to achieve this
goal. However, not enough research has been conducted in order to understand
the quality of the data generated through WiFi remote-positioning. This thesis
aims to address the issue and raise a warning light regarding the technology.
Crowd-movement analysis has classically been done manually, only at large
scales (spatial and temporal) and based on small samples. By automating the
process, we can dramatically increase the sample size, the amount of data. WiFi
remote-positioning is currently the most popular technology to achieve this
goal. However, not enough research has been conducted in order to understand
the quality of the data generated through WiFi remote-positioning. This thesis
aims to address the issue and raise a warning light regarding the technology.
| Original language | English |
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| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 21 Nov 2019 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-4896-0 |
| DOIs | |
| Publication status | Published - 21 Nov 2019 |