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Genetic Algorithm Based Microscale Vehicle Emissions Modelling

机译：基于遗传算法的微型汽车排放建模

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摘要

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.
机译：需要使排放估算的准确性与运输模型的输出相匹配。战略运输模型导致的长期交通预测中的总体错误率可能很大。微观模拟模型虽然具有高分辨率，但如果使用战略模型的输出来获得交通需求预测，则可能会有类似的测量误差。在微观层面上，本文讨论了现有排放估算方法的局限性。提出了用于预测CO2以外的排放污染物的排放模型。采用遗传算法的方法为黑匣子模型选择预测变量。该方法能够解决组合优化问题。总体而言，排放预测结果表明，所提出的新模型在准确性和鲁棒性方面优于常规方程式。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第25期|178490.1-178490.9|共9页
  • 作者

    Zhu Sicong; Tey LiSian; Ferreira Luis;

  • 作者单位

    Beijing Jiaotong Univ, Sch Traff & Transportat, MOE Key Lab Transportat Complex Syst Theory & Tec, Beijing 100044, Peoples R China;

    Univ Teknol MARA, Fac Civil Engn, Shah Alam 40450, Selangor, Malaysia;

    Univ Queensland, Fac Engn Architecture & Informat Technol, Sch Civil Engn, St Lucia, Qld 4072, Australia;

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