Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of...
Guardado en:
Autores principales: | Tong Zhu, Xiaohu Li, Wei Fan, Changshuai Wang, Haoxue Liu, Runqing Zhao |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Hindawi-Wiley
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e2e97a5be77d4336b48d20777a9a8414 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Influences of Exit Advance Guide Signs on the Trajectory and Speed of Passenger Cars in Highway Tunnels
por: Ting Shang, et al.
Publicado: (2021) -
Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information
por: Shijun Yu, et al.
Publicado: (2021) -
Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data
por: Cong Ding, et al.
Publicado: (2021) -
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
por: Bing Liu, et al.
Publicado: (2021) -
Driving Safety Assurance Method in Work Zone Crossovers of Highway Reconstruction and Expansion Project
por: Lu Lv, et al.
Publicado: (2021)