Traffic Forecasts Using Interacting Multiple Model Algorithm

Authors:
Yang Zhang;Yuncai Liu
Affiliations:
Research Center of Intelligent Transportation Systems, Shanghai Jiao Tong University, Shanghai, P.R. China 200240;Research Center of Intelligent Transportation Systems, Shanghai Jiao Tong University, Shanghai, P.R. China 200240
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

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Abstract

A predictor based on interacting multiple model (IMM) algorithm is proposed to forecast hourly travel time index (TTI) data in the paper. It is the first time to propose the approach to time series prediction. Seven baseline individual predictors are selected as combination components. Experimental results demonstrate that the IMM-based predictor can significantly outperform the other predictors and provide a large improvement in stability and robustness. This reveals that the approach is practically promising in traffic forecasting.