Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

Shengdong Mu

doi:10.1109/TII.2019.2929060

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Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

2019-12-01

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Shengdong Mu *

Xiong Zheng-xian

Tian Yixiang

DOI：10.1109/TII.2019.2929060

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Abstract

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.

Keywords:

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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