標題: A rolling-trained fuzzy neural network approach for freeway incident detection
作者: Lan, Lawrence W.
Huang, Yeh-Chieh
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: freeway incident detection;fuzzy neural network;rolling-trained fuzzy neural network
公開日期: 2006
摘要: This paper develops a rolling-trained fuzzy neural network (RTFNN) approach for freeway incident detection. The core logic of this approach is to establish a fuzzy neural network and to update the network parameters in response to the prevailing traffic conditions through a rolling-trained procedure. The simulation results of some thirty-six incident scenarios in a two-lane freeway mainline case study show that the proposed RTFNN approach can improve the detection performance over the fuzzy neural network approach, which is based on the same network structure but without updating the parameters through a rolling-trained procedure. The highest detection rate is found at a rolling horizon of 45 minutes and a training sample size of 90 samples in this case study.
URI: http://hdl.handle.net/11536/12842
ISSN: 1812-8602
期刊: TRANSPORTMETRICA
Volume: 2
Issue: 1
起始頁: 11
結束頁: 29
顯示於類別:期刊論文


文件中的檔案:

  1. 000243221900002.pdf

若為 zip 檔案,請下載檔案解壓縮後,用瀏覽器開啟資料夾中的 index.html 瀏覽全文。