原文传递
A Data-Driven Method for Congestion Identification and Classification
| 题名： | A Data-Driven Method for Congestion Identification and Classification |
| 正文语种： | eng |
| 作者： | Zarindast, Atousa;Poddar, Subhadipto;Sharma, Anuj |
| 作者单位： | Iowa State Univ Dept Civil & Environm Engn Ames IA 50010 USA;Iowa State Univ Dept Civil & Environm Engn Ames IA 50010 USA;Iowa State Univ Dept Civil & Environm Engn Ames IA 50010 USA |
| 关键词： | Recurrent and nonrecurrent congestion;Big data;Traffic congestion detection;Congestion classification |
| 摘要： | Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 148 |
| 期： | 4 |
| 页码： | 04022012.1-04022012.10 |
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