Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101783
Title: Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network
Authors: Asif, M. T.
Oran, A.
Fathi, E.
Xu, M.
Dhanya, M. M.
Mitrovic, N.
Jaillet, P.
Dauwels, Justin
Goh, Chong Yang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Asif, M. T., Dauwels, J., Goh, C. Y., Oran, A., Fathi, E., Xu, M., Dhanya, M. M., Mitrovic, N., & Jaillet, P. (2012). Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network. 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.983-988.
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.
URI: https://hdl.handle.net/10356/101783
http://hdl.handle.net/10220/16364
DOI: 10.1109/ITSC.2012.6338917
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

SCOPUSTM   
Citations 20

13
Updated on Jan 23, 2023

Page view(s) 50

534
Updated on Jan 27, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.