Download: PDF.
“Mining Sequential
Association Rules for Traveler Context Prediction”
by
Chad A. Williams,
Abolfazl Mohammadian,
Peter C. Nelson,
and
Sean T. Doherty.
In Proceedings of the First
International Workshop on Computational Transportation Science,
(Held at The International Conference on Mobile and Ubiquitous Systems:
Networks and Services (MOBIQUITOUS 2008), Dublin,
Ireland), July 2008.
A previous version appeared as
“Mining Sequential Association Rules For Traveler Context
Prediction”
by
Chad A. Williams,
Abolfazl Mohammadian,
Peter C. Nelson,
and
Sean T. Doherty.
University of Illinois at Chicago Department of Computer Science Technical
Report No. 2007.08.01-001 2007.08.01-001, Aug. 2007.
Recent work has focused on creating models for generating traveler behavior for micro simulations. With the increase in hand held computers and GPS devices, there is likely to be an increasing demand for extending this idea to predicting an individual's future travel plans for devices such as a smart traveler's assistant. In this work, we introduce a technique based on sequential data mining for predicting multiple aspects of an individual's next activity using a combination of user history and their similarity to other travelers. The proposed technique is empirically shown to perform better than more traditional approaches to this problem.
Keywords: Sequential mining, travel patterns, activity prediction
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BibTeX entry:
@inproceedings{WMND08,
author = {Chad A. Williams and Abolfazl Mohammadian and Peter C. Nelson
and Sean T. Doherty},
title = {Mining Sequential Association Rules for Traveler Context
Prediction},
booktitle = {Proceedings of the First International Workshop on
Computational Transportation Science},
address = {Held at The International Conference on Mobile and
Ubiquitous Systems: Networks and Services (MOBIQUITOUS 2008),
Dublin, Ireland},
month = jul,
year = {2008},
url = {http://cts.cs.uic.edu/iwcts.htm}
}