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Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining
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DOI：10.1109/TII.2019.2929060.png)
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摘要
En 中文
This article aims at discussing problems such as complex object types, large amount of data collection, high demand for transmission and calculation, and weak real-time scheduling and control ability in the construction of modern intelligent traffic information physical fusion network, cloud-based control system theory, modern intelligent traffic control network as the research object, and the physical design of the intelligent transportation information fusion cloud control system scheme. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on intelligent traffic flow data, in the center of the cloud control management server using deep learning and overrun learning machine intelligence study methods, such as the forecast of traffic flow data for training, to predict urban road short-term traffic flow and congestion. Further up in the air by using intelligent optimization scheduling algorithm for real-time traffic flow control strategy, the simulation results show the effectiveness of the proposed method.
Keyword:
Cloud computing
Control systems
Real-time systems
Predictive models
Transportation
Big Data
Job shop scheduling
Cloud control systems
cyber-physical
deep learning
extreme learning machine (ELM)
intelligent transportation
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