Chapter
Apr 26, 2012
HMM Based Fuzzy Model for Short-Term Traffic Forecasting
Authors: Gang Chang, Ph.D. [email protected], Yi Zhang [email protected], Danya Yao [email protected], and Yun Yue [email protected]Author Affiliations
Publication: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation
Abstract
This paper presents a Hidden Markov Model (HMM) based fuzzy model of short-term traffic forecasting. The model uses three sequential phases. Firstly, the HMM is used to partition the traffic flow data according to the ordering of the calculated log-likelihood values. Then, a recursive top-down algorithm is used to generate the minimum number of rules for accurate forecasting. Finally, a gradient descent method is applied to fine-tune the model parameters. To verify the method in real situation, an experimental model is constructed to produce traffic flow forecasting of freeway in CA, USA based on actual data.
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Published In
ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation
Pages: 1856 - 1862
Copyright
© 2011 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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Affiliations
Student, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, China 100084; and Lecturer, Academy of Military transportation, Tianjin, China, 300161.E-mail: [email protected]
Professor, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, China 100084.E-mail: [email protected]
Professor, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, China 100084.E-mail: [email protected]
Master Student, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, China 100084.E-mail: [email protected]
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