Enhancing Predictions in Signalized Arterials with Information on Short-Term Traffic Flow Dynamics
Short-term traffic flow predictions are an essential part of intelligent transportation systems. Previous research underlines the difficulty in systematically assessing the predictability of traffic flow near capacity or during congested conditions. In this article a neural network prediction scheme is proposed that is consistent with the pattern-based evolution of traffic flow and has the capability of exploiting past information to acquire knowledge on the traffic dynamics in order to enhance predictability. Findings indicate that pattern-based predictions are more accurate--in the traffic flow regimes considered--when compared to other local and global prediction techniques that operate under the time-series consideration. The pattern-based prediction scheme was also found to outperform the other methods tested in the knowledge of the anticipated traffic flow state in all traffic flow conditions considered.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
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Supplemental Notes:
- Abstract reprinted with permission from Taylor and Francis
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Authors:
- Vlahogianni, Eleni I
- Publication Date: 2009-4
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 73-84
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Serial:
- Journal of Intelligent Transportation Systems
- Volume: 13
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 1547-2450
- EISSN: 1547-2442
- Serial URL: http://www.tandfonline.com/loi/gits20
Subject/Index Terms
- TRT Terms: Arterial highways; Mathematical prediction; Neural networks; Signalized intersections; Time series; Traffic flow
- Subject Areas: Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01130142
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 17 2009 12:08PM