| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 24, 2010 |
| Latest Amendment Date: | May 13, 2011 |
| Award Number: | 1031452 |
| Award Instrument: | Standard Grant |
| Program Manager: |
elise miller-hooks
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2010 |
| End Date: | August 31, 2014 (Estimated) |
| Total Intended Award Amount: | $170,960.00 |
| Total Awarded Amount to Date: | $176,960.00 |
| Funds Obligated to Date: |
FY 2011 = $6,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
110 8TH ST TROY NY US 12180-3590 (518)276-6000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
110 8TH ST TROY NY US 12180-3590 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CIS-Civil Infrastructure Syst |
| Primary Program Source: |
01001112DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Mobile traffic sensors are those sensors that move with the flow they are monitoring, such as Global Positioning System (GPS) enabled devices or cellular phones. This research developed new concepts and methodologies that can be used to co-design transportation modeling methods and privacy protection techniques in collecting and using data collected from mobile traffic sensors. This resulted in a framework with privacy-aware transportation modeling and application-aware privacy protection, which has the potential to transform the way how mobile data are collected and used in transportation and other science and engineering fields.
The research was conducted as a multidisciplinary collaboration between transportation experts and location privacy experts, which benefited many audiences, including undergraduate and graduate students, transportation and location privacy researchers, and practitioners. Graduate and undergraduate students, especially those from underrepresented groups, participated in the research. Results from this research were used to enhance undergraduate and graduate level courses. The PI worked closely with transportation agencies and the industry to make his best effort to convey research findings to transportation decision makers, engineers, the industry, and the academic communities.
Major findings of the research are summarized as follows:
- Privacy definition: In an urban environment, traffic related privacy can be defined as the unlinkability of an individual vehicle over multiple locations (such as intersections).
- Virtual-trip-line (VTL) zone based privacy protection framework: If vehicle traces are only collected for the VTL zone (defined as the area between an upstream location and a downstream location around an intersection) for sample vehicles, supplemented by specially designed filtering algorithms to remove traces if the tracking probably of those traces are high, vehicle privacy can be reasonably protected. Furthermore, the collected traces are sufficient for urban traffic modeling applications.
- Mobile-data-based traffic modeling methods: Such methods require a proper integration of traffic principles/knowledge and advanced learning/optimization/statistical techniques.
- Mobile data for estimating transportation fuel consumption and emissions: mobile data, enhanced by newly developed modeling methods in this research to estimate the traces of un-sampled vehicles, can be used to accurately estimating vehicle emissions and energy use in urban areas.
Major outcomes of the research are summarized as follows:
- Research activities and results: this research produced eight (8) journal papers (see the detailed list below), one (1) book chapter, and 8 conference proceeding papers.
- Teaching activities: integrated research results to multiple courses the PI taught in the last three years, including: Traffic Engineering, Traffic Control and Simulation; and developed a new course on Advanced Transportation Models.
- Student advising: two (2) Ph.D. students were (partially) supported by this research, who graduated in the summer of 2013 and 2014 respectively
- Undergraduate research: Eight (8) undergraduate students worked on this research through the REU ...
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