交通运输工程与信息学报2024，Vol.22Issue(4)：166-181,16.DOI:10.19961/j.cnki.1672-4747.2024.04.017
基于多粒度级联森林的高排放重型柴油车辆的识别方法
Identification of high-emission heavy-duty diesel vehicles based on multigrained cascade forest
摘要
Abstract
Vehicle exhaust emissions are a major source of air pollution.On-board diagnostic(OBD)systems are important regulatory tools for vehicle emissions because they can directly access key in-formation related to nitrogen oxide(NOx)emissions.However,owing to data unavailability and qual-ity issues in OBD systems,accurately assessing the NOx emission levels of vehicles and effectively screening high-emission vehicles are challenging.This study proposes a method for screening high-emission heavy-duty diesel vehicles(HDDVs)using the multigrained cascade forest model.First,the Gumbel distribution is used to fit the probability density distribution of the ratio to determine the high-emission threshold and label high-emission records.Subsequently,the multicollinearity test is performed in conjunction with the entropy method to determine the optimal feature subsets.Next,the synthetic minority oversampling technique(SMOTE)is used to address the imbalance between high-emitting and clean samples.Finally,the multigrained cascade forest model is constructed to classify data with emissions that exceed the standards.Comparative-analysis experiments verify the effective-ness and applicability of the model in identifying high NOx emissions from HDDVs,thus enhancing the feasibility of identifying high-emission vehicles and providing reliable data support for the pre-cise regulation of vehicle emissions.关键词
信息技术/高排放车辆识别/多粒度级联森林模型/重型柴油车/车载诊断系统/Gumbel分布Key words
information technology/high-emitting vehicle identification/multi-Grained Cascade Forest/heavy-duty diesel vehicle/on-board diagnostics system/Gumbel distribution分类
资源环境引用本文复制引用
廖琳蔚,杨卓倩,杨鸿泰,韩科..基于多粒度级联森林的高排放重型柴油车辆的识别方法[J].交通运输工程与信息学报,2024,22(4):166-181,16.基金项目
中央高校基本科研业务费专项资金项目(2682023CX044,2682023ZTPY012) （2682023CX044,2682023ZTPY012）
自然科学基金面上项目(72071163) （72071163）
四川省国际科技合作项目(24GJHZ0342) （24GJHZ0342）
