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Quantifying errors in micro-scale emissions models using a case-study approach

机译：使用案例研究方法量化微型排放模型中的误差

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To quantify the level of uncertainty attached to forecasts of CO_2 emissions, an analysis of errors is undertaken; looking at both errors inherent in the model structure and the uncertainties in the input data. Both error types are treated in relation to CO_2 emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of traffic conditions using Monte Carlo simulation is used. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO_2 prediction, the interaction between the two components is taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a compromise solution is found. The results suggest that the CO_2 model used in the analysis produces low overall uncertainty under free flow traffic conditions. When average traffic speeds approach congested conditions, however, there are significant errors associated with emissions estimates.
机译：为了量化CO 2排放量预测的不确定性水平，进行了误差分析；查看模型结构固有的误差和输入数据的不确定性。使用来自澳大利亚布里斯班的案例研究，将两种误差类型都与CO_2排放建模相关。为了估计输入数据的不确定性，使用了使用蒙特卡洛模拟的交通状况分析。模型结构引起的不确定性也可以通过对多种交通场景的统计分析进行量化。为了获得最佳的总体CO_2预测，考虑了两个组件之间的相互作用。由于更复杂的模型不一定能产生更高的整体精度，因此找到了折衷方案。结果表明，在自由流动交通条件下，用于分析的CO_2模型产生的总体不确定性较低。但是，当平均行车速度接近拥挤状况时，与排放估算相关的重大误差。

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