关键词: 公路运输, 缺失数据插补, 随机森林, 公路隧道, 运营管理

Abstract:

Real- time & completely accessing and deeply mining of tunnel operational data such as environment state and traffic status is a foundation work to improve emergency response capacity and realize safety early warning. An imputation method is proposed based on Random Forest algorithm. Missing data set is separated according to missing features. Random Forest regression model is built to iteratively impute after the determination of stopping criterion. The optimal combination of decision tree numbers and variables numbers randomly sampled at each split in Random Forest are identified by taking the minimum normalized root mean square error as objective function. Imputation results on highway tunnel operational missing data indicate that the method provides significantly higher precision and better robustness than KNN, SVD, MICE, PPCA, reducing normalized root mean square error by at least 25% . Moreover, the imputation efficiency is improved significantly by using parallel computation. It covers the shortage of slow imputation speed and provides a warranty of effectiveness and timeliness in missing data imputation.

Key words: highway transportation, missing data imputation, Random Forest, highway tunnel, operation management