.png){kind=icon}
Return
Dynamic data driven transportation systems
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-016-4318-x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Congestion-induced delays and pollution in modern transportation systems remain formidable impediments to the sustainable growth of our cities. Next generation Intelligent Transportation Systems (ITS) will attack these problems by relying on extensive in-vehicle sensing, crowd-sourced data, ubiquitous computing, and communications to augment existing infrastructure-based deployments. Advances in wireless networking and mobile computing have made it possible to create dynamic, data driven application systems (DDDAS) that address many challenges in modern transportation systems. We outline a vision for future dynamic data-driven transportation systems, and focus on the effectiveness of an approach to real-time management based on online simulations. The online simulations are embedded in the traffic network where distributed simulators perform the modeling task individually but project the future states collectively. A real-time data driven arterial simulation methodology is proposed to assist such computations that are performed over a testbed in the midtown area of Atlanta, Georgia. Field results are presented that provide evidence to validate the proposed approach.
Keywords:
Data driven application systems
Real-time simulation
Transportation simulation
Distributed simulation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
View more