Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report

Wei Shen; Yiannis Kamarianakis; Laura Wynter; Jingrui He; Qing He; Rick Lawrence; Grzegorz Swirszcz

doi:10.1109/ICDMW.2010.52

Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report

Wei Shen, Yiannis Kamarianakis, Laura Wynter, Jingrui He, Qing He, Rick Lawrence, Grzegorz Swirszcz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages	1369-1371
Number of pages	3
DOIs	https://doi.org/10.1109/ICDMW.2010.52
State	Published - 2010
Externally published	Yes
Event	10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia Duration: Dec 14 2010 → Dec 17 2010

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Country/Territory	Australia
City	Sydney, NSW
Period	12/14/10 → 12/17/10

Keywords

Cross validation
Map-matching
Nearest neighbor

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICDMW.2010.52

Cite this

Shen, W., Kamarianakis, Y., Wynter, L., He, J., He, Q., Lawrence, R., & Swirszcz, G. (2010). Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report. In Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 (pp. 1369-1371). Article 5693455 (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2010.52

Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report. / Shen, Wei; Kamarianakis, Yiannis; Wynter, Laura et al.
Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010. 2010. p. 1369-1371 5693455 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shen, W, Kamarianakis, Y, Wynter, L, He, J, He, Q, Lawrence, R & Swirszcz, G 2010, Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report. in Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010., 5693455, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1369-1371, 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, 12/14/10. https://doi.org/10.1109/ICDMW.2010.52

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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.",

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AB - 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.

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Traffic velocity prediction using GPS data: IEEE ICDM Contest task 3 report

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

Access to Document

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