Unobserved Component Model for Predicting Monthly Traffic Volume
Traffic volume prediction plays a critical role in transportation system and infrastructure management. This paper develops the first application of an unobserved component model (UCM) for monthly traffic volume forecasting. The authors compare the UCM model with simple linear regression, autoregressive integrated moving average (ARIMA), support vector machine (SVM), and artificial neural network (ANN) models based on monthly traffic volume data from a key corridor in New Jersey. As a general econometric method, the UCM decomposes the time series into trend, seasonal, and irregular components, exhibiting superiority for statistically modeling traffic data with cyclic or seasonal fluctuations. The numerical analysis shows that the UCM outperforms all of the other four models and generates reasonably accurate prediction results. This research indicates that UCM can be considered as an alternative approach to modeling traffic volumes.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
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Supplemental Notes:
- © 2019 American Society of Civil Engineers.
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Authors:
- Bian, Zheyong
- Zhang, Zhipeng
- Liu, Xiang
- Qin, Xiao
- Publication Date: 2019-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 04019052
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Serial:
- Journal of Transportation Engineering, Part A: Systems
- Volume: 145
- Issue Number: 12
- Publisher: American Society of Civil Engineers
- ISSN: 2473-2907
- EISSN: 2473-2893
- Serial URL: http://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Econometric models; Monthly; Seasons; Statistical analysis; Traffic data; Traffic forecasting; Traffic models; Traffic volume
- Geographic Terms: New Jersey
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01723492
- Record Type: Publication
- Files: TRIS, ASCE
- Created Date: Nov 22 2019 4:48PM