AI-enabled Transportation Network Analysis, Planning and Operations
| 项目名称： | AI-enabled Transportation Network Analysis, Planning and Operations |
| 摘要： | Vehicle connectivity and automation would make vehicle trajectory data more readily available. The proposed research aims to leverage this dataset and recent advancements in implicit deep learning to develop an end-to-end modeling framework that would transform the way how metropolitan planning organizations (MPO) analyze, plan and manage their transportation networks. The proposed framework can directly take empirical, sampled trajectory data as inputs to learn drivers’ route choice behaviors and estimate traffic flow distribution across an urban traffic network. The proposed framework can further prescribe strategies such as lane direction configuration, parking provision, cordon pricing and perimeter control, to better manage the existing supply of urban traffic networks to reduce congestion |
| 状态： | Active |
| 资金： | 137014 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | University of Michigan Transportation Research Institute |
| 项目负责人： | Bezzina, Debra<==>Tucker-Thomas, Dawn |
| 执行机构： | University of Michigan, Ann Arbor |
| 主要研究人员： | Yin, Yafeng |
| 开始时间： | 20220401 |
| 预计完成日期： | 20230331 |
| 主题领域： | Administration and Management；Data and Information Technology；Operations and Traffic Management；Planning and Forecasting；Vehicles and Equipment |
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