Demand analysis and privacy of floating car data
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 creat...
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ndltd-uvic.ca-oai-dspace.library.uvic.ca-1828-111502019-09-14T16:39:42Z Demand analysis and privacy of floating car data Camilo, Giancarlo Wu, Kui floating car data privacy traffic demand traffic analysis area segmentation 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 2019-09-13T23:11:11Z 2019-09-13T23:11:11Z 2019 2019-09-13 Thesis http://hdl.handle.net/1828/11150 English en Available to the World Wide Web application/pdf |
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floating car data privacy traffic demand traffic analysis area segmentation |
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floating car data privacy traffic demand traffic analysis area segmentation Camilo, Giancarlo Demand analysis and privacy of floating car data |
description |
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 |
author2 |
Wu, Kui |
author_facet |
Wu, Kui Camilo, Giancarlo |
author |
Camilo, Giancarlo |
author_sort |
Camilo, Giancarlo |
title |
Demand analysis and privacy of floating car data |
title_short |
Demand analysis and privacy of floating car data |
title_full |
Demand analysis and privacy of floating car data |
title_fullStr |
Demand analysis and privacy of floating car data |
title_full_unstemmed |
Demand analysis and privacy of floating car data |
title_sort |
demand analysis and privacy of floating car data |
publishDate |
2019 |
url |
http://hdl.handle.net/1828/11150 |
work_keys_str_mv |
AT camilogiancarlo demandanalysisandprivacyoffloatingcardata |
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1719250817692729344 |