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CTS Events
SEMINAR
December 1, 2009 Dr. Ramasamy Uthurusamy, former General Director of Emerging Technologies, Information Systems and Services Division of General Motors Corporation, will present a seminar entitled "Role of Strategy in Computational Transportation Science",
SEMINAR
November 24, 2009 CTS welcomes Mr. Kevin Moran, NAVTEQ, who will present a seminar entitled "Digital Roadmaps, Driver Safety and Vehicle Efficiency - Smart Roads, Aware Drivers and Intelligent Vehicles - Closing the Loop with Digital Map-Enhanced Advanced Driver Assistance
SEMINAR
November 17, 2009 Josh Auld, CTS Fellow, will present a seminar entitled "Activity Planning Processes in the ADAPTS Activity-Based Modeling Framework", Tuesday, November 17th
CTS Happenings
November 3, 2009
Second International Workshop on
Computational Transportation Science
July 2, 2009
CTS IGERT Fellow Stephen Vaughn won a Research Grant for the 4th International Conference on Women's Issues in Transportation (2009)
May 18, 2009
CTS IGERT Fellow Josh Auld presented "Evaluating Transportation Impacts of Forecast Demographic Scenarios Using Population Synthesis and Data Transferability" at the 12th TRB National Transportation Planning Applications Conference Houston, TX in May
April 22, 2009
CTS-IGERT Trainee Chad A. William's collaborative work accepted for publication! "Attribute Constrained Rules For Partially Labeled Sequence Completion" Authors: Chad A. Williams, Peter C. Nelson, and Abolfazl Mohammadian.
April 22, 2009
CTS-IGERT Trainee Chad A. William's collaborative work accepted for publication! "Attribute Constrained Rules For Partially Labeled Sequence Completion" Authors: Chad A. Williams, Peter C. Nelson, and Abolfazl Mohammadian. This paper will appear in the Proceedings of the 9th Industrial Conference on Data Mining (ICDM 2009), Leipzig, Germany, July 2009. Abstract: Sequential pattern and rule mining have been the focus of much research in the data mining community, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it would greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets of attributes, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of sequence data are missing compared to traditional techniques, as demonstrated on a publicly available travel survey data set. |
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