Highway Traffic Flow Nonlinear Character Analysis and Prediction. (16th September 2015)
- Record Type:
- Journal Article
- Title:
- Highway Traffic Flow Nonlinear Character Analysis and Prediction. (16th September 2015)
- Main Title:
- Highway Traffic Flow Nonlinear Character Analysis and Prediction
- Authors:
- Hui, Meng
Bai, Lin
Li, YanBo
Wu, QiSheng - Other Names:
- Zhong Shouming Academic Editor.
- Abstract:
- Abstract : In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.
- Is Part Of:
- Mathematical problems in engineering. Volume 2015(2015)
- Journal:
- Mathematical problems in engineering
- Issue:
- Volume 2015(2015)
- Issue Display:
- Volume 2015, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 2015
- Issue:
- 2015
- Issue Sort Value:
- 2015-2015-2015-0000
- Page Start:
- Page End:
- Publication Date:
- 2015-09-16
- Subjects:
- Engineering mathematics -- Periodicals
510.2462 - Journal URLs:
- https://www.hindawi.com/journals/mpe/ ↗
http://www.gbhap-us.com/journals/238/238-top.htm ↗ - DOI:
- 10.1155/2015/902191 ↗
- Languages:
- English
- ISSNs:
- 1024-123X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10622.xml