摘要
为了通过科学方法优化交通管理,减少环境污染,提出了一种基于改进的K-means++聚类算法,结合马尔科夫链理论,对汽车行驶工况进行分析构建。对收集到的车辆行驶数据进行预处理,包括数据清洗和特征提取,通过主成分分析降低数据维度,引入基于余弦相似度度量的K-means++算法,通过肘部法则确定最佳聚类数目。结果表明,4类行驶工况能够有效模拟实际驾驶情况,通过聚类结果的平均轮廓系数对比证明,改进算法的聚类性能显著提升。利用马尔科夫链模型验证各工况之间的转移关系,构建最终汽车行驶工况。主要特征参数平均相对误差仅为4.726%,在模拟实际道路条件方面具有较高的合理性和准确性。
To optimize traffic management and reduce environmental pollution through scientific methods,a method for constructing vehicle driving conditions based on an improved K-means++clustering algorithm is proposed.Combined with Markov chain theory,this method analyzes and constructs vehicle driving conditions.The collected vehicle driving data are preprocessed,including data cleaning and feature extraction.Dimensionality reduction was performed using Principal Component Analysis(PCA),and a K-means++algorithm based on cosine similarity is introduced.The optimal number of clusters is determined using the elbow method.The results show that four driving conditions effectively simulate real driving scenarios.The comparison of average silhouette coefficients from the clustering results demonstrates that the improved algorithm significantly outperforms traditional methods in clustering performance.Using the Markov chain model,the transition relationships between the driving condition states are validated,and the final vehicle driving conditions are constructed.According to the comparative results of the relative error of key characteristic parameters,the average relative error is only 4.726%,indicating that this method has high rationality and accuracy in simulating actual road conditions.This provides a solution for traffic data analysis and model construction in complex traffic environments.
作者
陈俊杰
赵红
罗勇
丁晓云
田嘉昊
张泽谦
CHEN Junjie;ZHAO Hong;LUO Yong;DING Xiaoyun;TIAN Jiahao;ZHANG Zeqian(College of Mechanical and Electrical Engineering,Qingdao University,Qingdao 266071,China;Shenbang Intelligent Technology Group Co.,Ltd.,Qingdao 266041,China)
出处
《青岛大学学报(工程技术版)》
2025年第2期67-74,共8页
Journal of Qingdao University(Engineering & Technology Edition)
基金
青岛市科技惠民示范专项(24-1-8-cspz-18-nsh)。
关键词
聚类算法
汽车行驶工况
主成分分析
马尔科夫链
clustering algorithm
driving cycles
principal component analysis
markov chain
