A K-Nearest Neighbor Model of Light-Duty Vehicle Emission Factors Considering Pavement Roughness

Abstract

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 (K-NN) 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.

Measurement Models for Carbon Dioxide Emission Factors of Passenger Cars Considering Characteristics of Roads and Traffic

“…Li et al and Jiao built evaluation models to estimate vehicle emission factors with data concerning pavement conditions and found that the relationship between roadway pavement conditions and vehicle emissions was non-linear [ 15 , 16 ]. Peters et al explored approaches as to how to coordinate traffic signals to reduce congestion and carbon dioxide emissions [ 17 ].…”

How the Roadway Pavement Roughness Impacts Vehicle Emissions?

“…Recently, based on the on-road measured emission data on one dedicated light duty vehicle from the PEMS and the related pavement roughness information with a sampling interval of 5 m along more than 1,000 miles road tests in Texas, USA, Li et al [19] developed several nonlinear models such as the K-nearest neighbors (K-NN) and Neural Network models, for four major exhaust emission indexes (CO 2 , CO, HC and NO x .) The estimated emissions from those identified models are highly correlated to the real measured values.…”

Characterizing the Relationship between Carbon Dioxide Emissions and Vehicle Operating Modes on Roundabouts -A Pilot Test in a Single Lane Entry Roundabout

“…It seems that shortening the idling duration is determinative for the CO 2 emission reduction. However, a number of emission study results demonstrated that the CO 2 emission is highly associated with vehicles' operations [20,21].…”