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First published online January 1, 2010

Improved Vehicle-Specific Power Bins for Light-Duty Vehicles in Estimation of Carbon Dioxide Emissions in Beijing

Abstract

With increasingly wide recognition and acceptance of vehicle-specific power (VSP) as a viable variable in modeling vehicle emissions, the method for defining VSP bins is becoming critical. The primary objective of this study is to develop emission-specific VSP bins for estimating carbon dioxide (CO2) emissions for light-duty vehicles by using the real-world data collected in Beijing. The proposed method is developed by considering both emission characteristics and emission contributions of each bin. In this method, speed is chosen as the secondary parameter in defining bins because it has a higher impact on the CO2 emission rate than the engine stress for each VSP bin. With the proposed method, 24 VSP bins are eventually defined on the basis of the emission data collected in Beijing. In the proposed VSP bins, the data under VSP < 0 are grouped into one single bin because of their similar CO2 emission rates and comparable total emission contributions to other bins. Data at VSP = 0 are defined as an independent bin for characteristics that this single VSP point carries, such as its high VSP frequency, high total CO2 emission contributions, and significant lower emission rate than that of adjacent bins. On the basis of the validation of the proposed method, it is found that the use of an independent bin for VSP = 0 improves the accuracy of CO2 emission estimates and that the use of a single bin for VSP < 0, which simplifies the computational procedure, will not increase the error in the estimation of CO2 emissions.

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Article first published online: January 1, 2010
Issue published: January 2010

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© 2010 National Academy of Sciences.
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Authors

Affiliations

Yaofang Xu
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China.
Lei Yu
Beijing Jiaotong University; Department of Transportation Studies, Texas Southern University, 3100 Cleburne Avenue, Houston, TX 77004.
Guohua Song
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China.

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