title: Validation of temporal and spatial consistency of facility- and speed-specific vehicle-specific power distributions for emission estimation: A case study in Beijing, China.
abstract: Vehicle-specific power (VSP) has been found to be highly correlated with vehicle emissions. It is used in many studies on emission modeling such as the MOVES (Motor Vehicle Emissions Simulator) model. The existing studies develop specific VSP distributions (or OpMode distribution in MOVES) for different road types and various average speeds to represent the vehicle operating modes on road. However, it is still not clear if the facility- and speed-specific VSP distributions are consistent temporally and spatially. For instance, is it necessary to update periodically the database of the VSP distributions in the emission model? Are the VSP distributions developed in the city central business district (CBD) area applicable to its suburb area? In this context, this study examined the temporal and spatial consistency of the facility- and speed-specific VSP distributions in Beijing. The VSP distributions in different years and in different areas are developed, based on real-world vehicle activity data. The root mean square error (RMSE) is employed to quantify the difference between the VSP distributions. The maximum differences of the VSP distributions between different years and between different areas are approximately 20% of that between different road types. The analysis of the carbon dioxide (CO2) emission factor indicates that the temporal and spatial differences of the VSP distributions have no significant impact on vehicle emission estimation, with relative error of less than 3%.
  
  
  
  The temporal and spatial differences have no significant impact on the development of the facility- and speed-specific VSP distributions for the vehicle emission estimation. The database of the specific VSP distributions in the VSP-based emission models can maintain in terms of time. Thus, it is unnecessary to update the database regularly, and it is reliable to use the history vehicle activity data to forecast the emissions in the future. In one city, the areas with less data can still develop accurate VSP distributions based on better data from other areas.
authors.affiliation: a Ministry of Education Key Laboratory for Urban Transportation Complex Systems Theory and Technology , Beijing Jiaotong University , Beijing , People's Republic of China.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Zhai
authors.affiliation: a Ministry of Education Key Laboratory for Urban Transportation Complex Systems Theory and Technology , Beijing Jiaotong University , Beijing , People's Republic of China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Song
authors.affiliation: a Ministry of Education Key Laboratory for Urban Transportation Complex Systems Theory and Technology , Beijing Jiaotong University , Beijing , People's Republic of China.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Lu
authors.affiliation: b Project Manager-Assessment and Modelling, Beijing Transport Energy & Environment Centre , Beijing , People's Republic of China.
authors.forename: Weinan
authors.initials: W
authors.lastname: He
authors.affiliation: c College of Science, Engineering and Technology , Texas Southern University , Houston , TX , USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Yu
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000068476:Beijing; D002245:Carbon Dioxide / Q000032:analysis; D002681:China; D002947:Cities; D016208:Databases, Factual; D004784:Environmental Monitoring; D008962:Models, Theoretical; D018986:Motor Vehicles; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D001335:Vehicle Emissions; D002245:Carbon Dioxide