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2025 01 v.23 160-173
多源数据驱动的城市运渣车污染排放监管——以成都市为例
基金项目(Foundation): 国家自然科学基金面上项目(72071163); 四川省自然科学基金项目(2022NSFSC0474); 四川省科技计划项目(DQ202413); 中央高校基本科研业务费-科技创新项目(2682023CX044)
邮箱(Email):
DOI: 10.19961/j.cnki.1672-4747.2023.12.014
中文作者单位:

西南交通大学,经济管理学院;西南交通大学,交通运输与物流学院;四川国蓝中天环境科技集团有限公司;成都市环境保护科学研究院;成都市机动车排气污染防治技术保障中心;

摘要(Abstract):

【背景】我国重型柴油货车的尾气排放在各类车型中占比达70.4%(氮氧化物)和51.9%(颗粒物),是城市交通污染的重要来源。建筑垃圾运输车(又称“运渣车”)是我国大中型城市中最常见的一类重型柴油货车,这类车辆不仅在行驶过程中产生大量尾气排放,且常伴有扬尘污染,对其的管控被广泛纳入全国各地生态环境部门大气污染防治行动方案。【目标】本文围绕运渣车活动全周期监管,系统提出基于数智化技术的一体化监管体系,旨在进一步提升监管的时效性、靶向性、经济性。【方法】首先基于人工智能算法形成“车辆-运企-点位”一体化的数智化管控体系;其次开展该数智化体系在运渣车管控政策下的效果评估,重点分析车辆时空分布、行驶里程和排放的演化趋势,探讨政策发挥作用的机理。【数据】运渣车GPS轨迹数据、车载诊断系统(OBD)数据、尾气遥感监测系统数据。【应用】以环保为切入点,从数据、算法、政策、市场等维度对运渣车排放管控措施进行实践层面的探讨,并结合在成都市的实践案例,展现了该体系在推动我国城市大气环境治理和实现双碳目标中的重要参考价值。

关键词(KeyWords): 信息技术;机动车排污监控;数智化监管;运渣车;大气环境;城市治理
参考文献

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(1)根据参考文献[2],2021年全国机动车NO_x总排放量为582.1万吨,其中汽车占97.7%,而重型柴油货车NO_x排放量为400.1万吨,在所有汽车中占比400.4/582.1×97.7%=70.4%。2021年全国机动车PM总排放量为6.9万吨,其中汽车占92.2%,而重型柴油货车PM排放量为3.3万吨,在所有汽车中占比3.3/6.9×92.2%=51.9%。

(1)宏观F1分数(macro F1-score)是衡量模型在多类别分类任务中整体性能的指标。F1-score本质上是精确率(Precision)和召回率(Recall)的调和平均,用于权衡分类模型的准确性与覆盖率。而宏观F1分数是对各类别的F1-score取算术平均,能够反映模型在不同类别上的均衡性,尤其适用于类别分布不均衡的情况。

基本信息:

DOI:10.19961/j.cnki.1672-4747.2023.12.014

中图分类号:X734.2

引用信息:

[1]韩科,杨卓倩,牟华侨等.多源数据驱动的城市运渣车污染排放监管——以成都市为例[J].交通运输工程与信息学报,2025,23(01):160-173.DOI:10.19961/j.cnki.1672-4747.2023.12.014.

基金信息:

国家自然科学基金面上项目(72071163); 四川省自然科学基金项目(2022NSFSC0474); 四川省科技计划项目(DQ202413); 中央高校基本科研业务费-科技创新项目(2682023CX044)

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