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A novel arterial travel time distribution estimation model and its application to energy/emissions estimation
| 题名： | A novel arterial travel time distribution estimation model and its application to energy/emissions estimation |
| 正文语种： | 英文 |
| 作者： | Qichi Yang; Guoyuan Wu; Kanok Boriboonsomsin; Matthew Barth |
| 作者单位： | Google Inc., Mountain View; Centre for Environmental Research and Technology,University of California at Riverside; Centre for Environmental Research and Technology, University of California at Riverside; Centre for Environmental Research and Technology, University of California at Riverside |
| 关键词： | arterial travel time distribution; energy and emissions; Gaussian mixture model (GMM) |
| 摘要： | Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 4 |
| 页码： | 325-337 |
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