Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

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...

Full description

Saved in:
Bibliographic Details
Published in:Applied stochastic models in business and industry 2012-07, Vol.28 (4), p.297-315
Main Authors: Kamarianakis, Yiannis, Shen, Wei, Wynter, Laura
Format: Article
Language:English
Subjects:
adaptive LASSO
real-time predictions
threshold regressions
traffic forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.1937