SCIENCE CHINA Information Sciences, Volume 64 , Issue 3 : 139202(2021) https://doi.org/10.1007/s11432-018-9650-9

Deep amended COPERT model for regional vehicle emission prediction

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  • ReceivedAug 14, 2018
  • AcceptedOct 31, 2018
  • PublishedApr 13, 2020


There is no abstract available for this article.


This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61725304, 61673361).


Appendixes A–E.


[1] Smit R, Ntziachristos L, Boulter P. Validation of road vehicle and traffic emission models - A review and meta-analysis. Atmos Environ, 2010, 44: 2943-2953 CrossRef ADS Google Scholar

[2] Chang X, Chen B Y, Li Q. Estimating Real-Time Traffic Carbon Dioxide Emissions Based on Intelligent Transportation System Technologies. IEEE Trans Intell Transp Syst, 2013, 14: 469-479 CrossRef Google Scholar

[3] Zito P, Haibo Chen P, Bell M C. Predicting Real-Time Roadside CO and $\hbox{NO}_{2}$ Concentrations Using Neural Networks. IEEE Trans Intell Transp Syst, 2008, 9: 514-522 CrossRef Google Scholar

[4] Vardoulakis S, Fisher B E A, Pericleous K. Modelling air quality in street canyons: a review. Atmos Environ, 2003, 37: 155-182 CrossRef ADS Google Scholar

[5] Martin R V. Satellite remote sensing of surface air quality. Atmos Environ, 2008, 42: 7823-7843 CrossRef ADS Google Scholar

[6] van Donkelaar A, Martin R V, Park R J. Estimating ground-level PM$_{2.5}$ using aerosol optical depth determined from satellite remote sensing. J Geophys Res, 2006, 111: D21201 CrossRef ADS Google Scholar

[7] Xie Z, Zeng Z, Zhou G. Topic enhanced deep structured semantic models for knowledge base question answering. Sci China Inf Sci, 2017, 60: 110103 CrossRef Google Scholar

[8] Ntziachristos L, Samaras Z. COPERT II: Computer Programme to Calculate Emissions from Road Transport: User Manual. 1997. Google Scholar

[9] Kingma D P, Ba J. Adam: A method for stochastic optimization. 2014,. arXiv Google Scholar