Li Qing, Qiao Fengxiang, Yu Lei, Chen Shuyan, Li Tiezhu
a Innovative Transportation Research Institute, Texas Southern University , Houston , TX , USA.
b College of Transportation, Southeast University , Nanjing , Jiangsu , People's Republic of China.
J Air Waste Manag Assoc. 2018 Jun;68(6):564-575. doi: 10.1080/10962247.2017.1344744. Epub 2018 Apr 19.
In the United States, 26% of greenhouse gas emissions is emitted from the transportation sector; these emisssions meanwhile are accompanied by enormous toxic emissions to humans, such as carbon monoxide (CO), nitrogen oxides (NO), and hydrocarbon (HC), approximately 2.5% and 2.44% of a total exhaust emissions for a petrol and a diesel engine, respectively. These exhaust emissions are typically subject to vehicles' intermittent operations, such as hard acceleration and hard braking. In practice, drivers are inclined to operate intermittently while driving through a weaving segment, due to complex vehicle maneuvering for weaving. As a result, the exhaust emissions within a weaving segment ought to vary from those on a basic segment. However, existing emission models usually rely on vehicle operation information, and compute a generalized emission result, regardless of road configuration. This research proposes to explore the impacts of weaving segment configuration on vehicle emissions, identify important predictors for emission estimations, and develop a nonlinear normalized emission factor (NEF) model for weaving segments. An on-board emission test was conducted on 12 subjects on State Highway 288 in Houston, Texas. Vehicles' activity information, road conditions, and real-time exhaust emissions were collected by on-board diagnosis (OBD), a smartphone-based roughness app, and a portable emission measurement system (PEMS), respectively. Five feature selection algorithms were used to identify the important predictors for the response of NEF and the modeling algorithm. The predictive power of four algorithm-based emission models was tested by 10-fold cross-validation. Results showed that emissions are also susceptible to the type and length of a weaving segment. Bagged decision tree algorithm was chosen to develop a 50-grown-tree NEF model, which provided a validation error of 0.0051. The estimated NEFs are highly correlated with the observed NEFs in the training data set as well as in the validation data set, with the R values of 0.91 and 0.90, respectively.
Existing emission models usually rely on vehicle operation information to compute a generalized emission result, regardless of road configuration. In practice, while driving through a weaving segment, drivers are inclined to perform erratic maneuvers, such as hard braking and hard acceleration due to the complex weaving maneuver required. As a result, the exhaust emissions within a weaving segment vary from those on a basic segment. This research proposes to involve road configuration, in terms of the type and length of a weaving segment, in constructing an emission nonlinear model, which significantly improves emission estimations at a microscopic level.
在美国，26%的温室气体排放来自交通运输部门；与此同时，这些排放还伴随着大量对人体有害的排放物，如一氧化碳（CO）、氮氧化物（NO）和碳氢化合物（HC），分别约占汽油发动机和柴油发动机总尾气排放的2.5%和2.44%。这些尾气排放通常受到车辆间歇性运行的影响，如急加速和急刹车。在实际情况中，由于在交织路段需要进行复杂的车辆操控，驾驶员在通过交织路段时往往倾向于间歇性操作。因此，交织路段内的尾气排放应该与基本路段的排放有所不同。然而，现有的排放模型通常依赖于车辆运行信息，并计算出一个通用的排放结果，而不考虑道路配置。本研究旨在探讨交织路段配置对车辆排放的影响，确定排放估算的重要预测因素，并开发一种用于交织路段的非线性归一化排放因子（NEF）模型。在德克萨斯州休斯顿的288号州际公路上对12名受试者进行了车载排放测试。分别通过车载诊断（OBD）、基于智能手机的平整度应用程序和便携式排放测量系统（PEMS）收集车辆的活动信息、道路状况和实时尾气排放。使用五种特征选择算法来确定NEF响应和建模算法的重要预测因素。通过10折交叉验证测试了四种基于算法的排放模型的预测能力。结果表明，排放也容易受到交织路段类型和长度的影响。选择袋装决策树算法来开发一个包含50棵树的NEF模型，该模型的验证误差为0.0051。在训练数据集和验证数据集中，估计的NEF与观测到的NEF高度相关，R值分别为0.91和0.90。
现有的排放模型通常依赖于车辆运行信息来计算通用的排放结果，而不考虑道路配置。在实际中，驾驶员在通过交织路段时，由于需要进行复杂的交织操作，往往倾向于进行不稳定的操作，如急刹车和急加速。因此，交织路段内的尾气排放与基本路段的排放不同。本研究建议在构建排放非线性模型时考虑交织路段的类型和长度等道路配置因素，这在微观层面上显著提高了排放估算。
