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Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Author

Listed:
  • Tu Peng

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Xu Yang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Zi Xu

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Yu Liang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

Suggested Citation

  • Tu Peng & Xu Yang & Zi Xu & Yu Liang, 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods," Sustainability, MDPI, vol. 12(19), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8118-:d:422645
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    References listed on IDEAS

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    2. Mohammad Ali Sahraei & Keren Li & Qingyao Qiao, 2025. "A Multi-Stage Feature Selection and Explainable Machine Learning Framework for Forecasting Transportation CO 2 Emissions," Energies, MDPI, vol. 18(15), pages 1-26, August.
    3. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
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    6. Yongliang Liu & Chunling Tang & Aiying Zhou & Kai Yang, 2025. "A novel ensemble approach for road traffic carbon emission prediction: a case in Canada," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(7), pages 15977-16013, July.

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