Data Fusion to Improve the Accuracy of Multi-Modal Traffic Counts
| 项目名称： | Data Fusion to Improve the Accuracy of Multi-Modal Traffic Counts |
| 摘要： | Description: Current traffic counting systems often only measure one transportation mode accurately. In this project, the research team will improve the reliability and accuracy of video-based traffic counting technology by augmenting the video data with information extracted from other sensing technology. Additional data can originate from tube counters, magnetic loops, radar, vibration, and laser measurements. The project will use the raw data from the augmented sensors (with transient tube pressure signals) to count and classify vehicle types (FHWA 13 types, bicycles, and pedestrian traffic). Intellectual Merit: This project will evaluate the use of combined raw data from the tube-based vehicle counting/classification method and an integrated artificial neural network (ANN) to classify vehicle types with better accuracy than existing methods using data from one type of sensor. Broader Impacts: Improved data on the multi-modal movement of people and freight will provide transportation planners with better quantitative information on use of the existing system. Technology Transfer Plan: This research will be generating an implementation-ready hybrid traffic data collection tool for DOTs. |
| 状态： | Active |
| 资金： | $108524 |
| 资助组织： | Benedict College |
| 管理组织： | University of South Carolina, Columbia |
| 项目负责人： | Mullen, Robert L |
| 执行机构： | Benedict College |
| 主要研究人员： | Huynh, Nathan |
| 开始时间： | 20181201 |
| 预计完成日期： | 20200831 |
| 实际结束时间： | 0 |
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