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Data-driven models for microscopic vehicle emissions

Hajmohammadi, H; Marra, G; Heydecker, B; (2019) Data-driven models for microscopic vehicle emissions. Transportation Research Part D: Transport and Environment , 76 pp. 138-154. 10.1016/j.trd.2019.09.013.

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Abstract

In this paper, a new approach for describing the relationship between tailpipe emissions and vehicle movement variables is presented, called generalized additive model for location, scale and shape (GAMLSS). The dataset for this model is second-by-second emission laboratory measurements, following a real driving cycle that were recorded in urban, suburban and motorway areas of London. The GAMLSS emission model estimates each of CO_{2}, CO and NO_{x} in each second for two different vehicle types (petrol or diesel) using instantaneous speed and acceleration as the explanatory variables. Comparing the results with current emission models indicates substantial improvement in accuracy and quality of estimation by this approach.

Type: Article
Title: Data-driven models for microscopic vehicle emissions
DOI: 10.1016/j.trd.2019.09.013
Publisher version: https://doi.org/10.1016/j.trd.2019.09.013
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Vehicle emission modelling, GAMLSS approach, Air pollution
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10083290
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