Articles | Volume 22, issue 3
Atmos. Chem. Phys., 22, 1939–1950, 2022
Atmos. Chem. Phys., 22, 1939–1950, 2022
Research article
10 Feb 2022
Research article | 10 Feb 2022

High-resolution mapping of regional traffic emissions using land-use machine learning models

Xiaomeng Wu et al.

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Cited articles

Alam, I., Farid, D. M., and Rossetti, R. J.: The prediction of traffic flow with regression analysis, in: Emerging Technologies in Data Mining and Information Security, 661–671, Springer, Singapore, 2019. 
Boukerche, A. and Wang, J.: Machine Learning-based traffic prediction models for Intelligent Transportation Systems, Comput. Netw., 181, 107530,, 2020. 
Brokamp, C., Jandarov, R., Hossain, M., and Ryan, P.: Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model, Environ. Sci. Technol., 52, 4173–4179,, 2018. 
Chapman, L.: Transport and climate change: a review, J. Transp. Geogr., 15, 354–367,, 2007. 
Gately, C. K., Hutyra, L. R., and Sue Wing, I.: Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships, P. Natl. Acad. Sci. USA, 112, 4999–5004,, 2015. 
Short summary
Our work pioneered land-use machine learning methods for developing link-level emission inventories, utilizing hourly traffic profiles, including volume, speed, and fleet mix, obtained from the governmental intercity highway monitoring network in the "capital circles" of China. This research provides a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
Final-revised paper