河南科技大学学报（自然科学版）2025，Vol.46Issue(6)：38-48,11.DOI:10.15926/j.cnki.issn1672-6871.2025.06.005
大数据驱动下乘用车行驶工况构建方法研究
Research on the Construction Method of Passenger Car Driving Conditions Driven by Big Data
摘要
Abstract
To accurately characterize urban passenger vehicle driving patterns and address limitations in traditional driving scenario studies—such as limited sample sizes,poor clustering stability,and the omission of low-probability events during scenario synthesis—a big data-driven method for constructing driving scenarios is proposed.Using one year of OBD data from 200 passenger vehicles in Xi'an,a sample repository was built through data preprocessing and short-trip segmentation.Principal Component Analysis(PCA)was employed to reduce dimensionality to 16 feature parameters.The K-means++algorithm enhances clustering stability and accuracy.Markov Chain Monte Carlo(MCMC)optimizes cycle synthesis while preserving low-probability events.Results show the constructed candidate cycles exhibit an average relative error of 3.63%compared to raw data,significantly outperforming traditional methods:cluster-splicing(4.80%)the Markov chain method(5.60%),and standard driving cycles CLTC-P(8.74%)and WLTC(22.70%).关键词
大数据/行驶工况/短行程/K-means++聚类分析/马尔可夫链蒙特卡洛Key words
big data/short trip/kinematic segments/K-means++cluster analysis/Markov chain Monte Carlo分类
交通工程引用本文复制引用
杨阳,卫倩,于谦..大数据驱动下乘用车行驶工况构建方法研究[J].河南科技大学学报（自然科学版）,2025,46(6):38-48,11.基金项目
国家自然科学基金青年科学基金项目(52002032) （52002032）
陕西省自然科学基础研究计划面上项目(2025JC-YBMS-446) （2025JC-YBMS-446）
山西省回国留学人员科研资助项目(2024-126) （2024-126）
