Using a Random Subspace Predictor to Integrate Spatial and Temporal Information for Traffic Flow Forecasting

• Published in: Advances in Natural Computation pp. 652 - 655
• Main Authors: Sun, Shiliang, Zhang, Changshui
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Gaussian Mixture Model; Intelligent Transportation System; Markov Chain Model; Temporal Information; Transportation Network; Spatial data structures; Traffic flow; Probabilistic approach; Vector space; Transportation system; Transportation network; Robustness; Artificial intelligence; Forecasting; Intelligent system
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_93

Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow records may be partially missing or substantially contaminated by noise. In this paper, a robust traffic flow predictor, termed random subspace predictor, is developed integrating the entire spatial and temporal information in a transportation network to cope with this case. Experimental results demonstrate the effectiveness and robustness of the random subspace predictor.