Abstract

Currently, accurate traffic flow analysis and modeling are important key steps for intelligent transportation system (ITS). Missing traffic flow data are one of the most critical issues in the application of ITS. In this study, a hybrid method combining fuzzy rough set (FRS) and fuzzy neural network (FNN) is proposed for imputation of missing traffic data. Firstly, FNN is used for data classification, then the K-Nearest Neighbor (KNN) method is used to determine the optimal number of data used to estimate missing data in each category, and finally the fuzzy rough set is used to impute missing values. In order to validate the imputation performance of the proposed hybrid method, the traffic flow data collected from the loop detectors at different time intervals on roadway network are used in model calibration and validation. Three common indicators, including RMSE (root mean square error), R (correlation coefficient) and RA (relative accuracy), are used to evaluate the imputation performance under different data missing ratios. A model comparison is conducted between proposed imputation method and several widely used models including average-based and regression-based methods. The results show that the proposed method is superior to the traditional method for the traffic flow data collected at different time intervals with different missing ratios, which also further demonstrate its effectivity and validity.

摘要

目前，准确的交通流分析和建模是智能交通系统（ITS）的重要关键步骤。交通流数据缺失是ITS应用中最关键的问题之一。在这项研究中，提出了一种结合模糊粗糙集（FRS）和模糊神经网络（FNN）的混合方法来填补缺失的交通数据。首先使用FNN进行数据分类，然后使用K-Nearest Neighbor(KNN)方法确定用于估计每个类别缺失数据的最佳数据个数，最后使用模糊粗糙集进行缺失值插补。为了验证所提出的混合方法的插补性能，从道路网络上不同时间间隔的环路检测器收集的交通流数据用于模型校准和验证。三个常用指标，包括RMSE（均方根误差）、R（相关系数）和RA（相对精度），用于评估不同数据缺失率下的插补性能。在建议的插补方法和几种广泛使用的模型（包括基于平均值和基于回归的方法）之间进行模型比较。结果表明，对于不同时间间隔、不同缺失率的交通流数据，本文提出的方法优于传统方法，这也进一步证明了其有效性和有效性。在建议的插补方法和几种广泛使用的模型（包括基于平均值和基于回归的方法）之间进行模型比较。结果表明，对于不同时间间隔、不同缺失率的交通流数据，本文提出的方法优于传统方法，这也进一步证明了其有效性和有效性。在建议的插补方法和几种广泛使用的模型（包括基于平均值和基于回归的方法）之间进行模型比较。结果表明，对于不同时间间隔、不同缺失率的交通流数据，本文提出的方法优于传统方法，这也进一步证明了其有效性和有效性。