交通运输系统工程与信息

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (6): 20-25.

• 综合交通运输体系论坛 • 上一篇    下一篇

考虑道路属性特征的中观排放模型研究

吴克寒1，赵一新1，唐夕茹* 2   

  1. 1. 中国城市规划设计研究院，北京 100835；2. 北京城市系统工程研究中心，北京 100035
  • 收稿日期:2019-03-22 修回日期:2019-10-28 出版日期:2019-12-25 发布日期:2019-12-25
  • 作者简介:吴克寒(1988-)，男，辽宁沈阳人，工程师，博士.
  • 基金资助:

    国家重点研发计划/National Key Research and Development Project(2018YFB1601100, 2018YFB1601102).

Meso-level Vehicle Emission Model Based on Road Section Characteristics

WU Ke-han1, ZHAO Yi-xin1, TANG Xi-ru2   

  1. (1. China Academy of Urban Planning and Design, Beijing 100835, China; 2. Beijing Research Center of Urban System Engineering, Beijing 100035, China
  • Received:2019-03-22 Revised:2019-10-28 Online:2019-12-25 Published:2019-12-25

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摘要：

准确地对机动车排放污染进行量化评估是机动车排放控制策略制定的前提. 为提高模型计算精度，提出低速行驶比例的概念，用于描述车辆在路段上行驶时瞬时速度的分布特征. 在传统的VSP分布排放模型的基础上，纳入更多的中观影响因素，包括路段平均速度、路段长度、道路等级和低速行驶比例，采用多元回归方法构建一种新型的中观排放模型. 经比对，所建排放模型相对于传统VSP分布排放模型精度更高. 所建模型考虑的新增影响因素均较易获得，故可应用于城市大规模交通路网的排放计算，对提高城市路网排放计算精度，辅助机动车排放控制策略制定具有一定意义.

关键词: 城市交通, 排放模型, 多元回归, 道路车辆排放, 低速行驶比例, 多源数据

Abstract:

The accurate calculation of vehicle emissions is the prime behind developing the controlling methods of vehicles pollution. In order to improve the accuracy of the emissions model, the paper proposed a concept of low speed percentage. A new meso-level vehicle emissions model based on normal VSP model was built in this paper. More influence factors of road characters were introduced into the model, including vehicle average speed, road length, road level and the low speed percentage. The model was built with multiple regression analysis. The comparison results show the proposed model has a higher precision than the normal VSP model. In consideration of the new factors included in the proposed model were easily to access, the model could be used in the large-scale estimation of urban vehicle emissions. The proposed model is meaningful for improving the accuracy of vehicle emissions model and developing the controlling methods of vehicle pollution.

Key words: urban traffic, emission model, multiple regression, traffic emission, low speed percentage, multisource data

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