Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification
Short term traffic flow forecasting has received sustained attention for its ability to provide the anticipatory traffic condition required for proactive traffic control and management. Recently, a stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) process has gained increasing notice for its ability to jointly generate traffic flow level prediction and associated prediction interval. Considering the need for real time processing, Kalman filters have been utilized to implement this SARIMA + GARCH structure. Since conventional Kalman filters assume constant process variances, adaptive Kalman filters that can update the process variances are investigated in this paper. Empirical comparisons using real world traffic flow data aggregated at 15-min interval showed that the adaptive Kalman filter approach can generate workable level forecasts and prediction intervals; in particular, the adaptive Kalman filter approach demonstrates improved adaptability when traffic is highly volatile. Sensitivity analyses show that the performance of the adaptive Kalman filter stabilizes with the increase of its memory size. Remarks are provided on improving the performance of short term traffic flow forecasting.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Guo, Jianhua
- Huang, Wei
- Williams, Billy M
- Publication Date: 2014-6
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 50-64
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 43, Part 1
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Kalman filtering; Mathematical prediction; Real time data processing; Stochastic processes; Traffic congestion; Traffic flow rate; Traffic flow theory; Traffic forecasting; Uncertainty
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I71: Traffic Theory;
Filing Info
- Accession Number: 01531142
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 24 2014 3:18PM