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Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model

Author

Listed:
  • Xianbin Wang

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Yuqi Zhao

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Weifeng Li

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance.

Suggested Citation

  • Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2644-:d:1054436
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    References listed on IDEAS

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    1. Qiuyue Sai & Jun Bi & Dongfan Xie & Wei Guan, 2019. "Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    2. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, vol. 10(1), pages 1-20, January.
    3. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
    4. Yunjong Kim & Juneyoung Park & Cheol Oh, 2021. "A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    5. Chang-Gyun Roh & I-Jeong Im, 2020. "A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    6. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
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