An Ensemble Learning Short-Term Traffic Flow Forecasting with Transient Traffic Regimes

Abstract:

Article Preview

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:

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

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.