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A data-driven method of traffic emissions mapping with land use random forest models

Author

Listed:
  • Wen, Yifan
  • Wu, Ruoxi
  • Zhou, Zihang
  • Zhang, Shaojun
  • Yang, Shengge
  • Wallington, Timothy J.
  • Shen, Wei
  • Tan, Qinwen
  • Deng, Ye
  • Wu, Ye

Abstract

The development of intelligent approaches to quantify and mitigate on-road emissions is essential for urban and transportation sustainability for global megacities. Here, we utilize high-density traffic monitoring data and land use data to train random forest models capable of accurately predicting dynamic, link-level vehicle emissions. A total of 272 predicting indicators, including road features, population density, and land use information, were included in model training. Our model performed well, with a spatial generalization R2 > 0.8 for both volume and speed simulations. Dynamic link-based emissions of major air pollutants and carbon dioxide (CO2) were estimated for the whole road network of Chengdu, a populous city with the second greatest vehicle population in China. We adopted a generalized additive model to identify the drivers of spatial heterogeneity of on-road emissions and energy consumption, and nonlinear relationships between emissions, demographic and land use variables were found. Fine-grained assessments of emission reductions from potential Low Emission Zone policies are explored based on the high-resolution vehicle emission mapping tool. With high computational efficiency, the method is promising for handling traffic data streams in a real-time fashion, thus offering the potential for more precise vehicle emission management and carbon footprint tracking.

Suggested Citation

  • Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
  • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012289
    DOI: 10.1016/j.apenergy.2021.117916
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    1. Zhang, Shaojun & Wu, Ye & Liu, Huan & Huang, Ruikun & Yang, Liuhanzi & Li, Zhenhua & Fu, Lixin & Hao, Jiming, 2014. "Real-world fuel consumption and CO2 emissions of urban public buses in Beijing," Applied Energy, Elsevier, vol. 113(C), pages 1645-1655.
    2. Peng, Tianduo & Ou, Xunmin & Yuan, Zhiyi & Yan, Xiaoyu & Zhang, Xiliang, 2018. "Development and application of China provincial road transport energy demand and GHG emissions analysis model," Applied Energy, Elsevier, vol. 222(C), pages 313-328.
    3. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    4. Zeng, Yuan & Tan, Xianchun & Gu, Baihe & Wang, Yi & Xu, Baoguang, 2016. "Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets," Applied Energy, Elsevier, vol. 184(C), pages 1016-1025.
    5. Brand, Christian & Goodman, Anna & Rutter, Harry & Song, Yena & Ogilvie, David, 2013. "Associations of individual, household and environmental characteristics with carbon dioxide emissions from motorised passenger travel," Applied Energy, Elsevier, vol. 104(C), pages 158-169.
    6. Wen, Yifan & Zhang, Shaojun & Zhang, Jingran & Bao, Shuanghui & Wu, Xiaomeng & Yang, Daoyuan & Wu, Ye, 2020. "Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data," Applied Energy, Elsevier, vol. 260(C).
    7. Qian, Yong & Li, Zilong & Yu, Liang & Wang, Xiaole & Lu, Xingcai, 2019. "Review of the state-of-the-art of particulate matter emissions from modern gasoline fueled engines," Applied Energy, Elsevier, vol. 238(C), pages 1269-1298.
    8. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    9. Tang, Bao-Jun & Li, Xiao-Yi & Yu, Biying & Wei, Yi-Ming, 2019. "Sustainable development pathway for intercity passenger transport: A case study of China," Applied Energy, Elsevier, vol. 254(C).
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