A Functional Data Analysis Approach to Traffic Volume Forecasting
Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses functional principal components analysis to create high-quality online traffic volume forecasts. The methodology is validated with a data set of 1755 days of 15 min aggregated traffic volume time series. Compared with 365 randomly selected days, the functional forecasts are found to outperform traditional seasonal autoregressive integrated moving average-based methods in both count deviation and root mean squared error. In addition, through the functional data analysis approach the full exploitation of the continuous nature of the data can be achieved.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2018, IEEE.
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Authors:
- Wagner-Muns, Isaac Michael
- Guardiola, Ivan G
- Samaranayke, V A
- Kayani, Wasim Irshad
- Publication Date: 2018-3
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 878-888
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 19
- Issue Number: 3
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Data analysis; Functional analysis; Time series analysis; Traffic forecasting; Traffic models; Traffic volume
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01664591
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: Mar 28 2018 10:53AM