摘要
This study develops two Artificial Neural Network-based models to provide a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is notified. To select suitable data features, Genetic Algorithm is employed to decrease the number of model inputs while preserving relevant traffic characteristics with fewer inputs. This study shows the proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.
| 原文 | English |
|---|---|
| 主出版物標題 | 14th World Congress on Intelligent Transport Systems, ITS 2007 |
| 頁面 | 3773-3780 |
| 頁數 | 8 |
| 卷 | 5 |
| 出版狀態 | Published - 2007 |
| 事件 | 14th World Congress on Intelligent Transport Systems, ITS 2007 - Beijing, China 持續時間: 2007 10月 9 → 2007 10月 13 |
Other
| Other | 14th World Congress on Intelligent Transport Systems, ITS 2007 |
|---|---|
| 國家/地區 | China |
| 城市 | Beijing |
| 期間 | 07-10-09 → 07-10-13 |
All Science Journal Classification (ASJC) codes
- 機械工業
- 電氣與電子工程
- 控制與系統工程
- 運輸
- 汽車工程
- 電腦網路與通信
- 人工智慧
- 電腦科學應用