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

Jingjing Liu, Hongwei Ge, Jiajie Li (), Pengcheng He, Zhangang Hao () and Michael Hitch
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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

Energies, 2022, vol. 15, issue 19, 1-14

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.

Keywords: sustainable public transport; traffic sign recognition; convolutional neural network; maxout; k-means (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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