Mining Sequential Association Rules for Traveler Context Prediction

Chad A. Williams; Abolfazl (Kouros) Mohammadian; Peter C. Nelson; Sean T. Doherty

1st International ICST Workshop on Computational Transportation Science

Research Article

Mining Sequential Association Rules for Traveler Context Prediction

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/ICST.MOBIQUITOUS2008.3867,
    author={Chad A. Williams and Abolfazl (Kouros) Mohammadian and Peter C. Nelson and Sean T. Doherty},
    title={Mining Sequential Association Rules for Traveler Context Prediction},
    proceedings={1st International ICST Workshop on Computational Transportation Science},
    publisher={ACM},
    proceedings_a={IWCTS},
    year={2010},
    month={5},
    keywords={Sequential mining travel patterns activity prediction},
    doi={10.4108/ICST.MOBIQUITOUS2008.3867}
}

Chad A. Williams
Abolfazl (Kouros) Mohammadian
Peter C. Nelson
Sean T. Doherty
Year: 2010
Mining Sequential Association Rules for Traveler Context Prediction
IWCTS
ICST
DOI: 10.4108/ICST.MOBIQUITOUS2008.3867

Chad A. Williams¹^,a^,*, Abolfazl (Kouros) Mohammadian²^,*, Peter C. Nelson¹^,*, Sean T. Doherty³^,*

1: Department of Computer Science, University of Illinois at Chicago
2: Department of Civil Engineering, University of Illinois at Chicago
3: Department of Geography & Environmental Studies, Wilfrid Laurier University
a: This research was supported in part by the National Science Foundation IGERT program under Grant DGE-0549489.

*Contact email: cwilliam@cs.uic.edu, kouros@uic.edu, nelson@cs.uic.edu, sdoherty@wlu.ca

Abstract

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

Published: 2010-05-16
Publisher: ACM
Modified: 2010-05-16

: http://dx.doi.org/10.4108/ICST.MOBIQUITOUS2008.3867