Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network
This study applies Artificial Neural Network (ANN) for short-term prediction of traffic flow using past traffic data. The model incorporates traffic volume, speed, density, time, and day of week as input variables. Speed of each category of vehicles was considered separately as input variables in contrast to previous studies reported in literature which consider average speed of combined traffic flow. Results show that Artificial Neural Network has consistent performance even if time interval for traffic flow prediction was increased from 5 minutes to 15 minutes and produced good results even though speeds of each category of vehicles were considered separately as input variables.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
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Supplemental Notes:
- © 2013 Kranti Kumar et al.
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Authors:
- Kumar, Kranti
- Parida, M
- Katiyar, V K
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Conference:
- 2nd Conference of Transportation Research Group of India (2nd CTRG)
- Location: Agra , India
- Date: 2013-12-12 to 2013-12-15
- Publication Date: 2013-12-2
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 755-764
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Serial:
- Procedia - Social and Behavioral Sciences
- Volume: 104
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1877-0428
- Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Days; Forecasting; Neural networks; Traffic data; Traffic density; Traffic flow; Traffic speed; Traffic volume; Vehicle mix
- Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01503671
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 15 2014 9:28AM