This thesis investigates two research problems in analyzing floating car data (FCD): automated segmentation and privacy. For the former, we design an automated segmentation method based on the social functions of an area to enhance existing traffic demand analysis. This segmentation is used to create an extension of the traditional origin-destination matrix that can represent origins of traffic demand. The methods are then combined for interactive visualization of traffic demand, using a floating car dataset from a ride-hailing application. For the latter, we investigate the properties in FCD that may lead to privacy leaks. We present an attack on a real-world taxi dataset, showing that FCD, even though anonymized, can potentially leak privacy. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/11150 |
| Date | 13 September 2019 |
| Creators | Camilo, Giancarlo |
| Contributors | Wu, Kui |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.0019 seconds