Assessing carbon emissions from road transport through traffic flow estimators
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ML methods can help impute missing data for precise bottom-up estimation of GHG emissions and are applied in simulation models of vehicle emissions.
“Similarly, ML methods can help with imputing missing data for precise bottom-up estimation of GHG emissions [583] and they are also applied in simulation models of vehicle emissions [46].”
From Tackling climate change with machine learning (2023) · cites this paper as [583]
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References 65 · Cited by 67
- Authors
- Nocera, S., Ruiz-Alarcón-Quintero, C., & Cavallaro, F.
- Affiliations
- Università Iuav di Venezia (IT)
- Corresponding author
- Nocera, Silvio
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 95
- Pages
- 125-148
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2884250198
- Cited by
- 67
- References
- 65
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 9 · 2023: 8 · 2022: 6 · 2021: 11 · 2020: 17 · 2019: 3 · 2018: 1
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- AirIuav (Università Iuav di Venezia) (submittedVersion)
- PORTO@Iris (submittedVersion)
- Funders
- Spanish Ministry of Science and Innovation
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Vehicle emissions and performance
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- traffic flow estimation, missing data, combined model, vehicle type, road infrastructure, transportation planning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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