On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Space-time modeling of traffic flow
Computers & Geosciences
PPCA-based missing data imputation for traffic flow volume: a systematical approach
IEEE Transactions on Intelligent Transportation Systems
Network-scale traffic modeling and forecasting with graphical lasso
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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Smart transportation technologies require real-time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space-time dependencies of traffic variables. Nonlinearity is taken into account by a union of non-overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely the adaptive absolute shrinkage and selection operator (LASSO), is implemented to perform model selection and coefficient estimation simultaneously. Both the robust to outliers least absolute deviation estimates and conventional LASSO estimates are considered. The methodology is illustrated on 5-minute average speed data from three highway networks. Copyright © 2012 John Wiley & Sons, Ltd.