Bayesian Support Vector Regression for traffic speed prediction with error bars
Author(s)Gopi, Gaurav; Dauwels, Justin H. G.; Asif, Muhammad Tayyab; Ashwin, Sridhar; Mitrovic, Nikola; Rasheed, Umer; Jaillet, Patrick; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)
Institute of Electrical and Electronics Engineers (IEEE)
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.).
Author's final manuscript