Articles | Volume 16, issue 4
https://doi.org/10.5194/gmd-16-1427-2023
https://doi.org/10.5194/gmd-16-1427-2023
Model description paper
 | 
02 Mar 2023
Model description paper |  | 02 Mar 2023

Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions

Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler

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Cited articles

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Buch, N., Velastin, S. A., and Orwell, J.: A review of computer vision techniques for the analysis of urban traffic, IEEE T. Intell. Transp., 12, 920–939, 2011.  
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Chan, E. C. and Butler, T. M.: urbanChemFoam 1.0: large-eddy simulation of non-stationary chemical transport of traffic emissions in an idealized street canyon, Geosci. Model Dev., 14, 4555–4572, https://doi.org/10.5194/gmd-14-4555-2021, 2021. 
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Short summary
Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.