Impact of freeway weaving segment design on light-duty vehicle exhaust emissions

Document Type

Article

Publication Date

6-3-2018

Abstract

In the US, 26% of greenhouse gas emissions, e.g., CO, NOx, and HC, is emitted from the transportation sector. 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, including 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, resulting to variations in exhaust emissions within a weaving segment from those on a basic segment. To investigate this, the impacts of weaving segment configuration on vehicle emissions and the important predictors for emission estimations were assessed to 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, TX. Vehicles’ activity information, road conditions, and real-time exhaust emissions were collected by on-board diagnosis, a smartphone-based roughness app, and a portable emission measurement system, 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 were 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 NEF were highly correlated with the observed NEF in the training data set as well as in the validation data set. These results propose 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.

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