arbiter
JournalsPricingAccount

Estimation of Traffic Emissions by Integrating COPERT and CUBE

Gopika Vinayakumar

European Transport / Trasporti Europei2024https://doi.org/10.48295/et.2024.99.5article
ABDC C
Weight
0.37

What the paper says

Air pollution caused by motor vehicles is harmful to public health. Managing the cause of this type of pollution is very difficult. Effective prediction and management of pollution can be attained only by computing air pollution. It is difficult to measure emissions directly, so it is estimated using a variety of models developed worldwide. The current study made an attempt to integrate the emission model COPERT with the traffic assignment model in CUBE software for predicting the traffic-related emissions of pollutants SO2, NO2 & PM2.5 in mixed traffic conditions by considering passenger car and light duty vehicles (LDVS), two-wheeler, auto and bus. The study also aimed to include fuel variables and meteorological variable humidity along with fleet composition, average speed and average monthly temperature as inputs. This would enable us to control the rising vehicular emission, by assessing current and future air quality and thereby implementing policy decisions.

2 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.48295/et.2024.99.5

Or copy a formatted citation

@article{gopika2024,
  title        = {{Estimation of Traffic Emissions by Integrating COPERT and CUBE}},
  author       = {Gopika Vinayakumar},
  journal      = {European Transport / Trasporti Europei},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.48295/et.2024.99.5},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Estimation of Traffic Emissions by Integrating COPERT and CUBE

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.15 × 0.4 = 0.06
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.