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  • 标题:Statistical Vehicle Specific Power Profiling for Urban Freeways
  • 本地全文:下载
  • 作者:Zhuo Yao ; Zhuo Yao ; Heng Wei
  • 期刊名称:Procedia - Social and Behavioral Sciences
  • 印刷版ISSN:1877-0428
  • 出版年度:2013
  • 卷号:96
  • 页码:2927-2938
  • DOI:10.1016/j.sbspro.2013.08.324
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractVehicle Specific Power (VSP) is conventionally defined to represent the instantaneous vehicle engine power. It has been widely utilized to reveal the impact of vehicle operating conditions on emission and energy consumption estimates that are dependent upon speed, roadway grade and acceleration or deceleration on the basis of the second-by-second vehicle operation. VSP has hence been incorporated into a key contributing factor in the vehicle emission models including MOVES. To facilitate the preparation of MOVES vehicle operating mode distribution inputs, an enhanced understanding and modeling of VSP distribution versus roadway grade become indispensable. This paper presents a study in which previous studies are extended by deeply investigating the characteristics of VSP distributions and their impacts due to varying freeway grades, as well as time-of-day factors. Afterwards, statistical distribution models with a scope of bins is identified through a goodness of fit testing approach by using the sample data collected from the interstate freeway segments in Cincinnati area. The Global Positioning System (GPS) data were collected at a selected length of 30km urban freeway for AM, PM and Mid-day periods. The datasets representing the vehicle operating conditions for VSP calculation are then extracted from the GPS trajectory data. The distribution fit modeling results demonstrated that the Wakeby distribution with five parameters dominates the most fitting parameters with the samples. In addition, the speed variation lies behind the time of day differences is also identified to be a contributing factor of urban freeway VSP distribution.
  • 关键词:Vehicle Specific Power Distribution;Second-by-second GPS data
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