Unsupervised learning based performance analysis of v-support vector regression for speed prediction of a large road network
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Jaillet_Unsupervised learning.pdf
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Author(s)
Jaillet, Patrick
Asif, Muhammad Tayyab
Dauwels, Justin H. G.
Goh, C. Y.
Oran, Ali
Fathi, E.
Xu, M.
Dhanya, M. M.
Mitrovic, Nikola
Date Issued
September 2012
Journal
Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Asif, M. T. et al. “Unsupervised Learning Based Performance Analysis of N-Support Vector Regression for Speed Prediction of a Large Road Network.” IEEE, 2012. 983–988.
Version
Author's final manuscript
Abstract
Many intelligent transportation systems (ITS) applications require accurate prediction of traffic parameters. Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. However, these studies focus on highways, or a few road segments. We propose a robust and scalable method using v-SVR to tackle the problem of speed prediction of a large heterogeneous road network. The traditional performance measures such as mean absolute percentage error (MAPE) and root mean square error (RMSE) provide little insight into spatial and temporal characteristics of prediction methods for a large network. This inadequacy can be a serious hurdle in effective implementation of prediction models for route guidance, congestion avoidance, dynamic traffic assignment and other ITS applications. We propose unsupervised learning techniques by employing k-means clustering, principal component analysis (PCA), and self organizing maps (SOM) to overcome this insufficiency. We establish the effectiveness of the developed methods by evaluation of spatial and temporal characteristics of prediction performance of the proposed variable window v-SVR method.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ITSC.2012.6338917
