標題: | Short-term prediction of traffic dynamics with real-time recurrent learning algorithms |
作者: | Sheu, Jiuh-Biing Lan, Lawrence W. Huang, Yi-San 運輸與物流管理系 註:原交通所+運管所 Department of Transportation and Logistics Management |
關鍵字: | real-time recurrent learning;traffic dynamics;stochastic;deterministic |
公開日期: | 2009 |
摘要: | Short-term prediction of dynamic traffic states remains critical in the field of advanced traffic management systems and related areas. In this article, a novel real-time recurrent learning (RTRL) algorithm is proposed to address the above issue. We dabble in comparing pair predictability of linear method versus RTRL algorithms and simple non-linear method versus RTRL algorithms individually using a first-order autoregressive time-series AR(1) and a deterministic function. A field study tested with flow, speed and occupancy series data collected directly from dual-loop detectors on a freeway is conducted. The numerical results reveal that the performance of RTRL algorithms in predicting short-term traffic dynamics is satisfactorily accepted. Furthermore, it is found that the dynamics of short-term traffic states characterised in different time intervals, collected in diverse time lags and times of day may have significant effects on the prediction accuracy of the proposed algorithms. |
URI: | http://hdl.handle.net/11536/18165 http://dx.doi.org/10.1080/18128600802591681 |
ISSN: | 1812-8602 |
DOI: | 10.1080/18128600802591681 |
期刊: | TRANSPORTMETRICA |
Volume: | 5 |
Issue: | 1 |
起始頁: | 59 |
結束頁: | 83 |
顯示於類別: | 會議論文 |