原文传递
Automatic Incident Detection for Urban Expressways Based on Segment Traffic Flow Density
| 题名： | Automatic Incident Detection for Urban Expressways Based on Segment Traffic Flow Density |
| 其他题名： | Abdulhai,B.,&Ritchie,S.G.(1999).Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network.Transportation Research Part C,7(5),261–280. |
| 正文语种： | 英文 |
| 作者： | YANG CHENG |
| 关键词： | Automatic Incident Detection;Traffic Flow Density Fluctuation;Urban Expressway |
| 摘要： | Urban expressways play an important role in urban road networks. Although automatic incident detection (AID) methods have been studied for a long time, most of the existing AID algorithms are designed for freeways and do not explicitly consider the detection of incidents near ramps with frequent in and out weaving flows. Urban expressways usually have short spaced ramps, which makes the traffic flow characterizations quite different from the ones of freeways. In addition, expressways usually have heavy traffic flow, which makes it more difficult to distinguish incidents from congestion. This article presents an AID method for urban expressways, using loop detector data. Based on the geometric conditions and detector locations, the expressway is divided into short segments. The equivalent upstream and downstream traffic flow density difference is defined and calculated using the loop detector data. A detection logic based on the pattern of the density difference fluctuation is then proposed. The performance of the proposed algorithm was tested using real incident data from a corridor of the Shanghai expressway and compared with two classic AID algorithms. The results indicate that this method performs very well and is suitable for urban expressways. |
| 出版年： | 2015 |
| 论文唯一标识： | J-96Y2015V19N02009 |
| doi： | 10.1080/15472450.2014.977046 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 拼音刊名(出版物代码)： | J-96 |
| 卷： | 19 |
| 期： | 02 |
| 页码： | 205-213 |
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