Combining Virtual Reality and Machine Learning for Intelligent Sustainable Traffic Management
| 项目名称： | Combining Virtual Reality and Machine Learning for Intelligent Sustainable Traffic Management |
| 摘要： | Route choice models form the basis of traffic management systems. High Fidelity models that are based on rapidly evolving contextual conditions can have a huge impact on smart and energy efficient transportation. Existing route choice models are generic and are calibrated using static contextual conditions. These models do not take into account dynamic contextual conditions such as dynamic travel time, accessibility to nearest freeways, traffic incidents, and road closure due to an emergency. As a result, they can only make predictions at an aggregate level and for a generic set of contextual factors. There is a clear need to develop route choice models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions and policies. Hence, the objective of this study is to develop a novel context-aware framework that combines virtual reality with machine learning to improve understanding about driver�s decision-making with respect to route selection and prediction of roadway congestion in extreme events. This study aims to develop a powerful computation and analytic framework that integrates machine learning-based models with an immersive virtual environment, to improve the predictive power of existing models for traffic routing and resource allocation and deployment of resources (sensors, personnel, etc.). This will be achieved by taking into account contextual factors affecting human interaction with highway infrastructure. |
| 状态： | Completed |
| 资金： | 60000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Transportation Consortium of South-Central States |
| 项目负责人： | Hassan, Marwa M |
| 执行机构： | Louisiana State University and A&M College |
| 主要研究人员： | Gudishala, Ravindra |
| 开始时间： | 20180315 |
| 预计完成日期： | 20190915 |
| 实际结束时间： | 20190915 |
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