The goal of this research is to develop concepts and methodologies that can be used to co-design transportation modeling methods and privacy protection techniques in collecting and using data from mobile traffic sensors. Mobile sensors such as cell phones move with the flow they are monitoring as opposed to fixed-location sensor in the road infrastructure. They promise low-cost collection of traffic data but also raise privacy concerns since their information is more closely tied to individual vehicles. Building on a close collaboration of transportation researchers and location privacy experts, this research aims to answer the following two interrelated questions: (1) what form of mobile data to use and how their use will impact privacy; and (2) what methods should be used to protect mobile data privacy and what are their implications to data requirements for modeling Answering these questions will result in a framework with privacy-aware transportation modeling application-aware privacy protection, which can transform the way how mobile data are collected and used in transportation and many other science and engineering fields. This project builds on and will promote multidisciplinary collaborations, which benefit many audiences, including undergraduate and graduate students, transportation and location privacy researchers, and practitioners. Graduate and undergraduate students, especially those from underrepresented groups, can participate in the research. Results from this research will be used to enhance undergraduate and graduate level courses in both transportation engineering and computational privacy. Research findings can also help policy makers design proper policies/regulations on what mobile data to collect and how to better protect privacy. The PIs will work closely with transportation agencies and Standard groups and will make their best effort to convey research findings to transportation decision makers, engineers, the industry, and the academic communities.
|Effective start/end date||9/1/10 → 8/31/12|
- National Science Foundation (NSF)