An Online Change-Point-Based Model for Traffic Parameter Prediction
This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and density are subject to shifts because of weather, accidents, driving characteristics, etc. An intuitive approach of employing the hidden Markov model (HMM) and the expectation-maximization (EM) algorithm as change point models at these shifts and accordingly adapting the autoregressive-integrated-moving-average (ARIMA) forecasting model is formulated. The model is fitted and tested using publicly available 1993 I-880 loop data. It is compared with basic and mean updating forecasting models. Detailed numerical experiments are given on several days of data to show the impact of using change point models for adaptive forecasting models.
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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:
- Abstract reprinted with permission of IEEE.
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Authors:
- Comert, Gurcan
- Bezuglov, Anton
- Publication Date: 2013-9
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 1360-1369
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 14
- 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: Markov processes; Real time information; Traffic data; Traffic forecasting; Traffic models
- Uncontrolled Terms: Autoregressive integrated moving average models; Change point detection algorithms
- Subject Areas: Data and Information Technology; Highways; I71: Traffic Theory; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01524751
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: May 1 2014 4:36PM