A data-driven method of traffic emissions mapping with land use random forest models. (1st January 2022)

• Record Type: Journal Article
• Authors: Wen, Yifan; Wu, Ruoxi; Zhou, Zihang; Zhang, Shaojun; Yang, Shengge; Wallington, Timothy J.; Shen, Wei; Tan, Qinwen; Deng, Ye; Wu, Ye
• Is Part Of: Applied energy. Volume 305(2022)
• Publication Date: 2022-01-01
• Subjects: Data driven method -- Land use random forest -- Intelligent transportation systems -- Vehicle emissions -- Transportation sustainability; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117916
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Land use random forest model was used to map real-time link-level vehicle emissions. The model performed well with both high accuracy and computational efficiency. Spatial distributions of on-road CO2 and pollutants emissions were highly skewed. Drivers of spatial heterogeneity of on-road CO2 and NOX emissions were identified. Nonlinear relationships between population, urban form and emissions were found. 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 R 2 > 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.