| Paper: | SPTM-L2.4 | ||
| Session: | Networks and Communication Systems Modeling | ||
| Time: | Tuesday, May 18, 16:30 - 16:50 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | SWITCHING ARIMA MODEL BASED FORECASTING FOR TRAFFIC FLOW | ||
| Authors: | Guoqiang Yu; Tsinghua University | ||
| Changshui Zhang; Tsinghua University | |||
| Abstract: | Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where switching ARIMA model is a special case. Short-term forecasting of traffic flows is an essential part of Intelligent Traffic Systems (ITS). In this paper, we apply switching ARIMA model to traffic flow series. We have observed that the conventional switching model is inappropriate to describe the pattern changing. Thus the variable of duration is introduced and we use the sigmoid function to describe the influence of duration to the transition probability of the patterns. Based on the switching ARIMA model, the forecasting algorithm is presented. We apply the proposed model to the real data obtained from UTC/SCOOT systems in Traffic Management Bureau of Beijing. The experiments show that our proposed model is applicable and effective. | ||
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