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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Research on Optimization of Intelligent Traffic Dispatching Algorithms Based on Big Data in Chinese Urban Internet of Things Platform
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Research on Optimization of Intelligent Traffic Dispatching Algorithms Based on Big Data in Chinese Urban Internet of Things Platform

机译：Research on Optimization of Intelligent Traffic Dispatching Algorithms Based on Big Data in Chinese Urban Internet of Things Platform

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摘要

The economic development in China has brought about the urban traffic problems such as traffic congestion, long traffic waiting time, and inappropriate vehicle transfer. Therefore, under the technical background of China’s Internet of Things platform, the fusion technology based on big data is studied and an algorithm for Chinese urban intelligent traffic safety scheduling is designed. In this paper, first of all, the urban traffic safety big data is clustered. Secondly, the gray distribution model of the big data is established by extracting the association rule features. Thirdly, the elements in Chinese urban traffic safety big data are fused, including the text, location, picture, audio, and video. On the condition of meeting highly time-sensitive needs of urban traffic intelligence, the video information after data fusion is applied to detect traffic flow parameters, so that an evaluation strategy for urban traffic safety state under the urban traffic speed dispersion is established. According to the fuzzy value of urban traffic drivers’ satisfaction with the waiting time, the effect of traffic dispatching is measured, the convergence formula of urban traffic in the morning and evening peaks is constructed, and the optimal solution of the objective function of dispatching strategy is calculated by the particle swarm optimization algorithm. In this way, more efficient urban traffic safety scheduling in China is realized. As can be learned from the experimental results, the proposed algorithm can reasonably judge the urban traffic safety situation and reduce the time of waiting for urban vehicles with reasonable data fusion results, so it is proved to improve the urban traffic safety.

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