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Mathematical Problems in Engineering
Volume 2015, Article ID 178490, 9 pages
Research Article

Genetic Algorithm Based Microscale Vehicle Emissions Modelling

1MOE Key Laboratory for Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, 3 Shangyuan Cun, Haidian District, Beijing 100044, China
2Faculty of Civil Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
3School of Civil Engineering, Faculty of Engineering, Architecture and Information Technology, The University of Queensland, Brisbane St Lucia, QLD 4072, Australia

Received 27 August 2015; Revised 24 November 2015; Accepted 25 November 2015

Academic Editor: Ivanka Stamova

Copyright © 2015 Sicong Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.