Traffic Sensor Data Error Estimation with Statistical Learning Techniques
| 项目名称： | Traffic Sensor Data Error Estimation with Statistical Learning Techniques |
| 摘要： | Modern highway traffic management systems are greening the ways of mobility by increasing traffic safety, reducing road congestion, pollutant emission, and energy consumption. The efficient operation of the traffic management relies heavily on the quality of the highway sensor data input, which however, is not always reliable. Without appropriate treatment, the potential imperfection in the sensor data may significantly impede the correct traffic estimation and management decision. Most of the previous studies improved the reliability of sensor data by eliminating the sensors that are malfunctioning, at the cost of losing partial information. Instead of fully abandoning the erroneous sensor data, this research attempts to find an approach to process the data, so that the true traffic information can be recovered. The leading-edge statistical learning technologies provide a novel insight into the erroneous data treatment. By incorporating the spatial correlation of traffic counts from sensors in a roadway network, this research will develop a statistical learning based sensor error estimation framework, with the ability in quantifying and correcting the systematic errors of sensors. The corrected sensor data, with more reliable traffic information, contribute to more effective highway traffic management. |
| 状态： | Active |
| 资金： | 30000 |
| 资助组织： | National Center for Sustainable Transportation<==>Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Iacobucci, Lauren |
| 执行机构： | National Center for Sustainable Transportation<==>University of California, Davis |
| 开始时间： | 20200101 |
| 预计完成日期： | 20201231 |
| 主题领域： | Data and Information Technology；Highways；Operations and Traffic Management |
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