Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2010
Abstract
This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic evaluation framework for optimizing parameters and avoiding over-fitting. Our solution finished Second in the final evaluation.
Keywords
Cross validation, Map-matching, Nearest neighbor
Discipline
Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, Sydney, Australia, December 13
First Page
1369
Last Page
1371
ISBN
9780769542577
Identifier
10.1109/ICDMW.2010.52
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
SHEN, Wei; KAMARIANAKIS, Yiannis; WYNTER, Laura; HE, Jingrui; HE, Qing; LAWRENCE, Rick; and SWIRSZCZ, Grzegorz.
Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report. (2010). ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, Sydney, Australia, December 13. 1369-1371.
Available at: https://ink.library.smu.edu.sg/sis_research/10337
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDMW.2010.52