Genetic Algorithm Based Microscale Vehicle Emissions Modelling

• Main Authors: Ferreira, L. (Author), Tey, L. (Author), Zhu, S. (Author)
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2015
• Subjects: Algorithms; Combinatorial optimization; Combinatorial optimization problems; Emission estimation; Emissions estimation; Forecasting; Genetic algorithm approach; Genetic algorithms; Microsimulation models; Motor transportation; Optimization; Traffic forecasts; Transport models; Vehicle emission
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 1024123X (ISSN)
• DOI: 10.1155/2015/178490

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 COare 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. © 2015 Sicong Zhu et al.