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Author:

Liu, Ying (Liu, Ying.) | Song, Guo-Hua (Song, Guo-Hua.) | He, Wei-Nan (He, Wei-Nan.) | Cheng, Ying (Cheng, Ying.)

Indexed by:

EI CSCD

Abstract:

Developing speed correction factors (SCF) are important for the evaluation of the effect of speed variations on vehicle emissions. However, the existing methods is time consuming and costly, and the developed SCF are in low resolution. In order to develop the SCF in high resolution, this study develops a method for deriving SCF for Light-duty vehicles in Beijing based on vehicle trajectory data. Massive 60's speed-specific trajectories is divided from the vehicle activity data and grouped into speed-specific trajectory pools, and each trajectory pool is defined by the average speed with an equal interval of 2 km/h. Then, the vehicle-specific power (VSP) distribution is established for different speed and road types. Further, based on the emission rates and VSP distributions, the SCF for the pollutants are derived for different road types and emission standards. The analysis result shows that, the proposed method can generate SCF in higher resolution compared with the traditional methods, and it can capture more accurate traffic characteristics. Copyright © 2017 by Science Press.

Keyword:

Speed Roads and streets Systems engineering Trajectories Neutron emission Vehicles

Author Community:

  • [ 1 ] [Liu, Ying]Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Song, Guo-Hua]Beijing Jiaotong University, Beijing; 100044, China
  • [ 3 ] [He, Wei-Nan]Beijing Transport Institute, Beijing; 100073, China
  • [ 4 ] [Cheng, Ying]Beijing Transport Institute, Beijing; 100073, China

Reprint Author's Address:

  • [liu, ying]beijing university of technology, beijing; 100124, china

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Source :

Journal of Transportation Systems Engineering and Information Technology

ISSN: 1009-6744

Year: 2017

Issue: 4

Volume: 17

Page: 214-220

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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