Paper Title:
An Ensemble Learning Short-Term Traffic Flow Forecasting with Transient Traffic Regimes
  Abstract

Short-term traffic flow forecasting has a rich and substantive research history. For layered hybrid forecasting approaches, traffic flow series are classified into distinct regimes based on characteristics of different traffic conditions so that regime-specific forecasting models can be trained to adequately and consistently approximate dynamic traffic dynamic behaviors. However, traffic regimes are inherently changing and transient, making it a challenging task to perform regime-specific forecasting. In this paper, an ensemble learning approach is proposed and regime-based forecasting models are developed to address the nonlinearity and non-stationarity of traffic flow data. Case study results demonstrate that the proposed approach can effectively account for transient regimes and produce accurate online short-term traffic forecasts.

  Info
Periodical
Chapter
Chapter 6: ITS Theory and Applications
Edited by
Shucai Li
Pages
849-853
DOI
10.4028/www.scientific.net/AMM.97-98.849
Citation
J. S. Zhu, "An Ensemble Learning Short-Term Traffic Flow Forecasting with Transient Traffic Regimes", Applied Mechanics and Materials, Vols. 97-98, pp. 849-853, 2011
Online since
September 2011
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Price
$32.00
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