Bayesian Support Vector Regression for traffic speed prediction with error bars
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Jaillet_Bayesian support.pdf
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800.88 KB
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Author(s)
Gopi, Gaurav
Dauwels, Justin H. G.
Asif, Muhammad Tayyab
Ashwin, Sridhar
Mitrovic, Nikola
Rasheed, Umer
Jaillet, Patrick
Date Issued
October 2013
Journal
Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Gopi, Gaurav, Justin Dauwels, Muhammad Tayyab Asif, Sridhar Ashwin, Nikola Mitrovic, Umer Rasheed, and Patrick Jaillet. “Bayesian Support Vector Regression for Traffic Speed Prediction with Error Bars.” 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) (n.d.).
Version
Author's final manuscript
Abstract
Traffic prediction algorithms can help improve the performance of Intelligent Transportation Systems (ITS). To this end, ITS require algorithms with high prediction accuracy. For more robust performance, the traffic systems also require a measure of uncertainty associated with prediction data. Data driven algorithms such as Support Vector Regression (SVR) perform traffic prediction with overall high accuracy. However, they do not provide any information about the associated uncertainty. The prediction error can only be calculated once field data becomes available. Consequently, the applications which use prediction data, remain vulnerable to variations in prediction error. To overcome this issue, we propose Bayesian Support Vector Regression (BSVR). BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in anticipating variations in prediction error. We perform multi-horizon prediction and analyze the performance of BSVR for expressways as well as general road segments.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ITSC.2013.6728223
