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

计量
- 文章访问数: 21
- HTML全文浏览量: 2
- PDF下载量: 1
- 被引次数: 0
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

HTML全文

参考文献(27)

[1]	中华人民共和国生态环境部. 中国移动源环境管理年报(2022年)[R]. 2022.
[2]	张晔, 宋国华, 尹航, 等. 综合交通运输系统碳排放预测的不确定性分析[J]. 交通运输工程与信息学报, 2023, 21(1): 64-79.
[3]	ZHANG Z, SONG G, CHEN J, et al. Development of a simplified model of speed-specific vehicle-specific power distribution based on vehicle weight for fuel consumption estimates[J]. Transportation Research Record, 2020, 2674(12): 52-67.
[4]	WANG X, SONG G H, ZHAI Z Q, et al. Effects of vehicle load on emissions of heavy-duty diesel trucks: a study based on real-world data[J]. International Journal of Environmental Research and Public Health, 2021, 18(8): 3877.
[5]	胥耀方, 于雷, 郝艳召, 等. 机动车尾气排放宏观模型开发与应用初探[J]. 交通运输系统工程与信息, 2009, 9(2): 147-154.
[6]	WANG Q D, HUO H, HE K B, et al. Characterization of vehicle driving patterns and development of driving cycles in Chinese cities[J]. Transportation Research Part D: Transport and Environment, 2008, 13(5): 289-297.
[7]	RAKHA H, AHN K, TRANI A. Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions[J]. Transportation Research Part D: Transport and Environment, 2004, 9(1): 49-74.
[8]	DAVIS N. IVE model users manual version 2.0[R]. University of California at Riverside, 2008.
[9]	U.S. Environmental protection agency. MOVES2010 Highway Vehicle: population and activity Data: EPA-420-R-10-026[R]. 2010.
[10]	DUARTE G O, GONÇALVES G A, FARIAS T L. Analysis of fuel consumption and pollutant emissions of regulated and alternative driving cycles based on real-world measurements[J]. Transportation Research Part D: Transport and Environment, 2016, 44: 43-54.
[11]	李晨旭. 基于行驶轨迹的城市机动车排放热点区域污染物时空分布研究[D]. 北京: 北京交通大学, 2020.
[12]	JIMÉNEZ-PALACIOS J L. Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing[D]. Massachusetts Institute of Technology, 1999.
[13]	SONG G H, YU L, WU Y Z. Development of speed correction factors based on speed-specific distributions of vehicle specific power for urban restricted-access roadways[J]. Journal of Transportation Engineering, 2016, 142(3):4016001.
[14]	SONG G H, YU L, TU Z. Distribution characteristics of vehicle-specific power on urban restricted-access roadways[J]. Journal of Transportation Engineering, 2012, 138(2): 202-209.
[15]	US EPA. Population and activity of on-road vehicles in MOVES2014: EPA-420-R-16-003a[R]. 2016.
[16]	柏洋洋, 何超, 李加强, 等. 基于GPS数据的重型柴油货车排放时空特征分析[J]. 环境工程, 2023, 41(4): 63-70 ,100.
[17]	ZHOU B Y, HE L Q, ZHANG S M, et al. Variability of fuel consumption and CO₂ emissions of a gasoline passenger car under multiple in-laboratory and on-road testing conditions[J]. Journal of Environmental Sciences, 2023, 125: 266-276.
[18]	王晓宁, 于思源, 郭术林. 基于低温工况速度变化的CAL3QHC模型修正[J]. 环境工程, 2020, 38(11): 130-134.
[19]	CHOUDHARY A, GOKHALE S. Urban real-world driving traffic emissions during interruption and congestion[J]. Transportation Research Part D: Transport and Environment, 2016, 43: 59-70.
[20]	HE Z B, ZHANG W Y, JIA N. Estimating carbon dioxide emissions of freeway traffic: a spatiotemporal cell-based model[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1976-1986.
[21]	殷子渊, 张凯山. 典型城市轻型汽油车尾气排放模式分析[J]. 环境工程, 2021, 39(4): 64-71.
[22]	王清洲, 栾海敏, 范鑫, 等. 高速公路主线收费站节能减排测算模型与实例分析[J]. 环境工程, 2019, 37(6): 184-189.
[23]	赵琦, 于雷, 宋国华. 轻型车与重型车高速公路比功率分布特征研究[J]. 交通运输系统工程与信息, 2015, 15(3): 196-203.
[24]	中华人民共和国公安部. 道路交通拥堵度评价方法[S]. 2020.
[25]	陈喜群. 交通流动态随机演化模型研究[D]. 北京: 清华大学, 2012.
[26]	中华人民共和国工业和信息化部, 公安部. 汽车、挂车及汽车列车外廓尺寸、轴荷及质量限值: GB 1589—2016[S]. 2016.
[27]	中华人民共和国国务院. 中华人民共和国道路交通安全法(修订)[R]. 2021.