交通运输工程与信息学报2025，Vol.23Issue(2)：122-135,14.DOI:10.19961/j.cnki.1672-4747.2024.12.002
基于改进密度峰值聚类的交通扰动事件分级评价方法
A classification and evolution method for traffic disturbance events based on improved density peak clustering
摘要
Abstract
[Background]The rapid and accurate identification of the severity and potential impact of traffic disturbance events is critical for developing scientific response measures and optimizing man-agement strategies.However,existing event classification methods are heavily reliant on expert knowledge or human experience,making them susceptible to subjective bias,limiting the objectivity and effectiveness of the evaluation results.[Objective]To address the issues of strong subjectivity and insufficient automation in current classification and evaluation methods for traffic disturbance events as well as to achieve rapid and objective grading of the impact of such events.[Methods]A genetic algorithm-based density peak clustering(GA-DPC)method is proposed,which consists of three majorsteps:(1)identifying slope change inflection points in decision values to automatically de-termine the initial cluster centers and number of clusters,(2)formulating an optimization problem to maximize the Silhouette Index(SI)and solve for the optimal cutoff distance using a genetic algo-rithm,and(3)iteratively updating the cluster centers and cluster numbers based on the optimal cutoff distance to obtain the final clustering results.[Data]Tests were conducted using public datasets such as Spiral,R15,and ThreeCircles,simulated traffic accident data,and real-world rainfall disturbance data.[Results]On the public test datasets,the GA-DPC method outperformed ADPC,traditional DPC,K-means,and DBSCAN based on the classification metrics SI and Calinski-Harabasz(CH)val-ues.The GA-DPC also demonstrated superior performance in both the SI and CH metrics in the clas-sification and evaluation of traffic accidents and rainfall disturbance events.[Application]The GA-DPC is a data-driven analytical tool for traffic management departments that allows them to rapidly and objectively assess the severity of traffic disturbance events and their impact on transportation sys-tems.The GA-DPC promotes informed decision-making for resource allocation and the development of emergency management strategies.关键词
城市交通/交通扰动事件/密度峰值聚类算法/分级评价Key words
urban traffic/traffic disturbance event/density peak clustering/hierarchical evaluation分类
交通工程引用本文复制引用
鲍震天,郑凡非,郑芳芳..基于改进密度峰值聚类的交通扰动事件分级评价方法[J].交通运输工程与信息学报,2025,23(2):122-135,14.基金项目
国家重点研发计划项目(2021YFB1600100) （2021YFB1600100）
