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

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

Applied Energy, 2022, vol. 305, issue C, No S0306261921012289

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.

Keywords: Data driven method; Land use random forest; Intelligent transportation systems; Vehicle emissions; Transportation sustainability (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2021.117916

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