PhD Research Fellow in transport mode detection with location privacy (305597) | University of Oslo
Project description
Automatic human activity detection (HAR), mainly transport-wise (transport mode detection), has been gaining interest since 2000. It allows us to better plan our cities, and new approaches have been made possible with the widespread use of smartphones carrying several different sensors. As a matter of fact, in most developed countries, smartphone penetration exceeds 80%. The automatic transport mode detection (TMD), when effectively exploited, possibly using some kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys.
This data is valuable to several stakeholders: i) architects and urban planners when designing cities or roads, ii) public transportation systems (e.g., as an automatic way for issuing tickets for the travelers, particularly when they are using public transport means such as train, bus, subway, etc.), iii) mobile users with a need to navigate, iv) healthcare (e.g., it is a useful way of detecting abnormal activities of patients with Alzheimer, dementia or any other mental pathologies, and v) entertainment, and more (e.g., a football match, a national festival or a bad weather condition may change the regular public transportation demands in a particular period of time). In addition, the information obtained by TMD systems is essential for providing mobile users with various value-added services, without tracking the user, for location-privacy. This proposal aims at solving the challenge of location-privacy by developing a) a system that can perform TMD with the capacity of generalization (not significantly affected by the geographical location, user variation, smartphone type, or smartphone position, e.g., in a bag or pocket), without tracking the user for location-privacy.
Software lies at the core of all IT-systems. The Programming Section, in which the candidate should be integrated, performs research and teaching on how to develop high quality software that benefits society and that is useful for both individuals and organizations.
Development plan:
In this PhD project you will develop a system that will be able to perform TMD, without the GPS to ensure location-privacy, with the capacity of generalization (not significantly affected by the geographical location, user variation, smartphone type, or smartphone position, e.g., in a bag or pocket).
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