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

Yiannis Kamarianakis, Wei Shen, Laura Wynter

Research output: Contribution to journalArticle

47 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)297-315
Number of pages19
JournalApplied Stochastic Models in Business and Industry
Issue number4
StatePublished - Jul 1 2012



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

ASJC Scopus subject areas

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

Cite this