深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升.本文以芝加哥2016-2021年的101452条追尾事故数据为研究对象,将原始数据按照7:3随机分为训练集和测试集.在训练集数据上,利用生成式插补网络(Generative Adversarial Imputation Network,GAIN)实现对缺失数据的填补.为对比不同数据填补方法的效果,同时选择多重插补(Multiple Imputation by Chained Equations,MICE)算法、期望最大化(Expectation Maximization,EM)填充算法、缺失森林(MissForest)算法和K最近邻(K-Nearest Neighbor,KNN)算法对同一数据集进行数据填补,并基于填补前后变量方差变化比较不同填补算法对数据变异性的影响.在完成数据填补的基础上,构建LightGBM三分类事故严重程度影响因素分析模型.使用原始训练集数据,以及填补后的训练集数据分别训练模型,并使用未经填补的测试集数据检验模型预测效果.结果表明,经缺失值填补后,模型性能得到一定改善,使用GAIN填补数据集训练的模型,相较于原始数据训练的模型,准确率提高了6.84％,F1提高了4.61％,AUC(Area Under the Curve)提高了10.09％,且改善效果优于其他4种填补方法.
Rear-end Crash Data Imputation Methods Using Generative Adversarial Networks
A meticulous analysis of traffic crash data can furnish pivotal theoretical foundations for averting crashes and mitigating their severity.However,data collection,transmission,and storage processes frequently engender data missingness,which consequently diminishes the accuracy of statistical analyses and elevates the risk of model misjudgments.In this research,a dataset comprising 101452 rear-end crashes between 2016 and 2021 in Chicago was examined.The original data was randomly divided into training and testing sets at a ratio of 7:3.For the training data,missing values were imputed using a Generative Adversarial Imputation Network(GAIN).To foster a comparative assessment of various data imputation algorithms,alternative methods—including Multiple Imputation by Chained Equations(MICE),Expectation Maximization(EM)imputation,MissForest algorithm,and K-Nearest Neighbor(KNN)algorithm—were concurrently applied to the identical dataset.Subsequently,the variance alterations pre and post-imputation were analyzed to gauge the differential impacts of these methodologies on data variability.Post the fulfillment of data imputation,a three-category LightGBM model targeting crash severity analysis was constructed.Models trained with both the original and the imputed training data were established.And the original testing data were used to test the performances of different models.The results indicated that the model performance was improved after missing data imputation.The model trained with the GAIN-augmented training data manifested a 6.84％increment in accuracy,a 4.61％increment in the F1 score,and a 10.09％increment in the AUC(Area Under the Curve),thereby surpassing the improvements facilitated by the other four imputation algorithms.
urban trafficdata imputationgenerative adversarial networksrear-end crashesLightGBM model
万方数据
维普
