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How Can Sustainable Public Transport Be Improved? A Traffic Sign Recognition Approach Using Convolutional Neural Network

Author

Listed:
  • Jingjing Liu

    (School of Management, Shandong Technology and Business University, Yantai 264005, China)

  • Hongwei Ge

    (School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China)

  • Jiajie Li

    (Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Pengcheng He

    (School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China)

  • Zhangang Hao

    (School of Management, Shandong Technology and Business University, Yantai 264005, China)

  • Michael Hitch

    (Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Perth, WA 6845, Australia)

Abstract

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport.

Suggested Citation

  • Jingjing Liu & Hongwei Ge & Jiajie Li & Pengcheng He & Zhangang Hao & Michael Hitch, 2022. "How Can Sustainable Public Transport Be Improved? A Traffic Sign Recognition Approach Using Convolutional Neural Network," Energies, MDPI, vol. 15(19), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7386-:d:936378
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    References listed on IDEAS

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