Sparse kernel regression for traffic flow forecasting

Authors:
Rongqing Huang;Shiliang Sun;Yan Liu
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science and Technology, East China Normal University, Shanghai, P.R. China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

Citing 7
Cited 1

Predicting Time Series with Support Vector Machines

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Consistency of Trace Norm Minimization

The Journal of Machine Learning Research
Convex multi-task feature learning

Machine Learning
Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting

IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A bayesian network approach to traffic flow forecasting

IEEE Transactions on Intelligent Transportation Systems
The Selective Random Subspace Predictor for Traffic Flow Forecasting

IEEE Transactions on Intelligent Transportation Systems

Kernel regression with sparse metric learning

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new kernel regression algorithm with sparse distance metric is proposed and applied to the traffic flow forecasting. The sparse kernel regression model is established by enforcing a mixed (2, 1)-norm regularization over the metric matrix. It learns a mahalanobis metric by a gradient descent procedure, which can simultaneously remove noise in data and lead to a low-rank metric matrix. The new model is applied to forecast short-term traffic flows to verify its effectiveness. Experiments on real data of urban vehicular traffic flows are performed. Comparisons with two related kernel regression algorithms under three criterions show that the proposed algorithm is more effective for short-term traffic flow forecasting.