Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2012

Abstract

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.

Keywords

adaptive LASSO, real-time predictions, threshold regressions, traffic forecasting

Discipline

Computer Engineering | Transportation

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Applied Stochastic Models in Business and Industry

Volume

28

Issue

4

First Page

297

Last Page

315

ISSN

1524-1904

Identifier

10.1002/asmb.1937

Publisher

Wiley

Additional URL

https://doi.org/10.1002/asmb.1937

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