Modelling of instantaneous emissions from diesel vehicles with a special focus on NOₓ: Insights from machine learning techniques
Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, the authors apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOₓ emissions resulted in 17 clusters capturing 88% of trips in the dataset. The authors show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, they evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOₓ predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are −0.01 (s.d. 0.02) and −0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that the authors present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOₓ emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00489697
-
Supplemental Notes:
- © 2020 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
-
Authors:
- Le Cornec, Clémence M A
- Molden, Nick
- van Reeuwijk, Maarten
- Stettler, Marc E J
- Publication Date: 2020-10-1
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: 139625
-
Serial:
- Science of the Total Environment
- Volume: 737
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0048-9697
- Serial URL: http://www.sciencedirect.com/science/journal/00489697
Subject/Index Terms
- TRT Terms: Cluster analysis; Computer models; Diesel engine exhaust gases; Machine learning; Neural networks; Nitrogen oxides
- Subject Areas: Environment; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01856045
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 24 2022 3:05PM