公路货运瓶颈节点污染物时空分布特征分析

段开心; 宋国华; 翟志强; 卢淼

doi:10.13205/j.hjgc.202410011

公路货运瓶颈节点污染物时空分布特征分析

doi: 10.13205/j.hjgc.202410011

北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室, 北京 100044

基金项目:

国家重点研发计划(2018YFB1600701)

详细信息

作者简介:
段开心(2000-),女,硕士研究生,主要研究方向为交通环境、交通大数据。22120814@bjtu.edu.cn

通讯作者:
宋国华(1980-),男,教授,主要研究方向为交通规划、交通环境、交通仿真。ghsong@bjtu.edu.cn

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出版历程
- 收稿日期: 2023-12-03
- 网络出版日期: 2024-11-30

SPATIOTEMPORAL EMISSION CHARACTERISTICS ANALYSIS IN BOTTLENECK NODES OF HIGHWAY FREIGHT

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China

摘要

摘要: 研究公路货运瓶颈节点的污染物形成机理与分布特征可为货运高排区域的大气污染联防联控提供理论依据。通过采集北京市重点货运路网的逐秒行驶轨迹数据、重型柴油车排放测试数据,依据拥堵形成消散机理,设计基于行驶工况(减速、加速、怠速)的排放源强量化指标,进而构建货车拥堵时空排放模型。最后,以北京市通州区西集综合检查站为案例,分析该瓶颈节点的CO₂、CO、THC、NO_x时空分布特征,并对比设站前后以及采取减排措施下时空污染物变化情况。结果表明:瓶颈节点的排放总量是常规的2.2~2.5倍。瓶颈区前排队区域以及驶离线后0~10 m区域排放强度最大,为减速驶入和瓶颈作业区域的2.1~2.9倍。抽检与提升检测站服务效率均在一定程度上改善了排放,且2种减排措施对NO_x改善效果最为显著。
- 公路货运 /
- 交通瓶颈 /
- 排放时空分布模型 /
- 重型货车 /
- 排放源强 /
- 行驶工况
Abstract: Studying the emission generation mechanism and dispersion features in the bottleneck nodes of highway freight can provide a theoretical basis for pollution joint prevention and control in highway emission hotspots. To achieve the dynamic portrayal of spatial-temporal emissions, second-by-second driving data, and heavy-duty diesel vehicle emission test data were collected from Beijing’s key freight road network, then, based on the characterization of congestion formation and dissipation, a quantification index of emission intensity in different driving conditions (deceleration, acceleration, and idling) was designed. Furthermore, an emission spatial-temporal distribution model based on the vehicle specific power was built. Finally, taking the Xiji Comprehensive Inspection Station in Tongzhou District Beijing as a case, we compared the changes in the spatial-temporal emissions(CO₂, CO, THC, NO_x) before and after the establishment of the station, and the adoption of two emission reduction measures(sampling and enhancing service efficiency). The results showed that the total emissions at the bottleneck nodes were 2.2 to 2.5 times higher than the conventional ones. The queuing area in front of the bottleneck and 0~10 m behind it had the maximum emission intensity, which was 2.1 to 2.9 times higher than the decelerated and bottleneck operating areas. To a certain extent, sampling and enhancing service efficiency can reduce polution emissions. Moreover, both of the emission reduction strategies significantly improve NO_x emission reduction effect.
- highway freight /
- traffic bottleneck /
- spatial-temporal distribution model of emission /
- heavy-duty vehicles /
- emission intensity /
- driving conditions

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