NDLI: Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network

Content Provider	IEEE Xplore Digital Library
Author	Asif, M.T. Dauwels, J. Goh, C.Y. Oran, A. Fathi, E. Xu, M. Dhanya, M.M. Mitrovic, N. Jaillet, P.
Copyright Year	2012
Description	Author affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore (Asif, M.T.; Dauwels, J.; Goh, C.Y.; Fathi, E.; Xu, M.; Dhanya, M.M.; Mitrovic, N.) \|\| Center for Future Urban Mobility, Singapore-MIT Alliance for Research and technology, 117543 (Oran, A.) \|\| Laboratory for Information and Decision Systems, MIT, Cambridge, MA, USA (Jaillet, P.)
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 n-SVR to tackle the problem of speed prediction of a large heterogenous 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 n-SVR method.
Starting Page	983
Ending Page	988
File Size	758252
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Principal component analysis Artificial neural networks Training Kernel Market research Prediction methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in