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Research on the improvement of transportation efficiency of smart city by traffic visualization based on pattern recognition

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
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Abstract

In order to improve the traffic visualization management technology of smart cities, this paper applies machine learning to smart traffic management, and uses machine learning methods to process and analyze traffic data generated in smart cities. Moreover, this paper uses it to understand the distribution law of traffic data and the internal connections between data, excavate traffic characteristics in smart cities, and accurately predict the future traffic situation, so as to solve the problems of traffic congestion and route planning in smart cities. Moreover, this paper combines improved machine learning algorithms to construct a traffic visualization management system, and designs experiments to verify the performance of the system proposed in this paper. It can be seen from the experimental research results that the method proposed in this paper can play an important role in the traffic management of smart cities.

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Authors and Affiliations

  1. Cloud Walk Technology Co., Ltd., Guangzhou, 511466, Guangdong, China

    Yong Zhang

  2. Smart City Research Institute, Beijing Intelligent Control Information Technology Society, Beijing, 100191, China

    Hao Wang

  3. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100011, China

    Xuede Wang

Authors
  1. Yong Zhang
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  2. Hao Wang
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  3. Xuede Wang
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Correspondence to Yong Zhang.

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Zhang, Y., Wang, H. & Wang, X. Research on the improvement of transportation efficiency of smart city by traffic visualization based on pattern recognition. Neural Comput & Applic 35, 2211–2224 (2023). https://doi.org/10.1007/s00521-022-07222-4

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  • DOI: https://doi.org/10.1007/s00521-022-07222-4

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