Rate models for predicting vehicular emissions of nitrogen oxides (NO X ) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X ) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.

中文翻译：

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用于车辆排放预测的多元自适应回归样条模型

用于预测氮氧化物（NO X）车辆排放的费率模型对车辆的运行模式（例如巡航，加速，减速和怠速）不敏感，因为这些模型通常基于平均行驶速度。这项研究证明了使用其他变量（例如车速，加速度，负载，功率和环境温度）来预测（NO X）排放以确保排放清单准确并因此设计和实施空气质量建模和管理计划的可行性适当地。我们建议使用非参数Boosting-多元自适应回归样条（B-MARS）算法来提高多元自适应回归样条（MARS）建模的准确性，以便根据车载测量结果有效预测车辆的NO X排放量，底盘测功机测试。然后，将B-MARS方法应用于NO X排放估算。该模型方法提供了更可靠的估算结果，并提供了更好的NO X排放预测。因此，结果表明，B-MARS方法是预测NO X排放的有用且相当准确的工具，监管机构可能会采用它。该模型方法提供了更可靠的估算结果，并提供了更好的NO X排放预测。因此，结果表明，B-MARS方法是预测NO X排放的有用且相当准确的工具，监管机构可能会采用它。该模型方法提供了更可靠的估算结果，并提供了更好的NO X排放预测。因此，结果表明，B-MARS方法是预测NO X排放的有用且相当准确的工具，监管机构可能会采用它。