Non- Linear Models of Light-Duty Vehicle Emission Factors Considering Pavement Roughness
- *Corresponding Author:
- Feng-Xiang Qiao
Ph.D, Professor, Innovative Transportation Research Institute Texas Southern University
3100 Cleburne Street, Houston, Texas, 77004, USA
E-mail: [email protected]
Received Date: December 22, 2017; Accepted Date: February 20, 2017; Published Date: February 24, 2017
Citation: Li Q, Qiao F, Yu L (2017) Non- Linear Models of Light-Duty Vehicle Emission Factors Considering Pavement Roughness. J Civil Environ Eng 7:268. doi: 10.4172/2165-784X.1000268
Copyright: © 2017 Qing L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Emission factors are very important measures for developing an emission inventory, making decisions, designing control strategies, mitigating climate change, and even improving public health, in terms of respiratory system diseases. The emission factors could be either measured from field tests or estimated by an emission model. Existing models seldom consider the impacts of some special factors such as pavement roughness. As the impacts of the pavement roughness on emissions are very complicated, a linear model or physical model may not depict the mappings from affecting factors to resulted emission factors. In this paper, two non-linear models, including K-Nearest Neighbor (KNN) and Neural Network (NN) were built to estimate vehicle emission factors using roughness involved input data. A best fitted model was identified to illustrate the emission pattern along a wide range of pavement roughness. Multiple field tests were conducted in five regions of the State of Texas, United States, with a total of 1,609 km test length. One dedicated test vehicle was employed throughout the test. Pavement roughness was tested using a smartphone based application. All tested data were separated into four groups, each representing a different range of roughness, while the modeling was conducted within each group. The predictive performance of each model was evaluated by (1) correlation coefficient; (2) relative errors; and (3) two tailed unequal variance t-test. Results suggest that, K-NN can be better than NN to model the emission factors for the Texas highway system, and driving on a smoother and rougher pavement result in higher vehicle emissions.