Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
| 项目名称： | Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation |
| 摘要： | Work zones constitute 24 percent of non-recurring congestion (FHWA, 2014). It is important to study driver behaviors (e.g., headway, speed, lane changing, etc.) in response to actual work zone scenarios (e.g., roadway geometry, traffic, traffic control strategy, etc.) to understand the potential work zone impacts (e.g., delay/queue and conflicts) and to develop appropriate traffic control strategies to manage and/or reduce these work zone impacts. Work zone traffic simulation models have been developed to quantitatively evaluate work zone impacts by considering driving behavior (e.g., vehicle headway, speed profile, merging location and time, vehicle lateral offset, etc.) and work zone scenarios. However, the driving behavior data for the southeastern region at a detailed level to support simulation models is currently lacking; this hinders the reliability and accuracy of simulation results. Using video log images together with artificial intelligence provides a promising alternative for cost-effectively acquiring detailed driving behavior data (e.g., vehicle headway, speed profile, merging location and time, vehicle lateral offset, etc.). Therefore, this study focuses on assessing the accuracy of such data and quantifying the impacts of this data on the simulation outcomes. The objectives of this project are to 1) assess the accuracy of the video-based driving behavior data and the factors contributing to the errors, and 2) evaluate the impacts of such data on the simulation results. Experimental tests will be conducted using a light tower to simulate the camera configuration in a work zone to identify the factors contributing to errors in the derived driving behavior data and to quantify these errors. Other sensing technologies, such as LiDAR and laser distance measuring devices, will be used to establish the ground truth of the roadway geometry and distance between objects. Sensitivity analyses will be conducted to quantitatively evaluate the impacts of such data on the simulation results. The outcome of this project will identify and quantify the accuracy of individual driving behaviors, the factors affecting the accuracy, and the impact of such data on the simulation results. |
| 状态： | Active |
| 资金： | 107799 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE) |
| 项目负责人： | Tucker-Thomas, Dawn |
| 执行机构： | Auburn University |
| 主要研究人员： | Tsai, Yichang (James) |
| 开始时间： | 20180815 |
| 预计完成日期： | 20200815 |
| 实际结束时间： | 0 |
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