摘要: |
Connected and autonomous vehicles (CAV) are an important means by which the US can improve the safety, environmental compatibility, economics, and equity of personal transportation. This research seeks to synthesize both rich vehicle-level datasets derived from experiments with CAV sensors and systems and the state of the art transportation-system level datasets to compose second-by-second vehicle-level Lagrangian predictions of vehicle velocity trajectories, applicable to CAVs. We will seek to understand the role of advanced traffic management systems (ATMS) (and other infrastructure) sensors, information, and infrastructure in advancing the safety and environmental benefits of CAVs. |