On Missing Traffic Data Imputation Based on Fuzzy C-Means Method by Considering Spatial–Temporal Correlation

Abstract

The lack of some traffic flow data seriously affects the quality of data collection and analysis in the traffic system. Completing the missing data is one of the most important steps in achieving the functions of intelligent transportation systems. In this paper an approach based on fuzzy C-means (FCM) imputes missing traffic volume data in loop detectors. With spatial–temporal correlation between detectors, the conventional vector-based data structure is first transformed into a matrix-based data pattern. Then, the genetic algorithm is applied to optimize the parameters of cluster size and weighting factor in the FCM model. Finally, the actual traffic flow volume collected at different locations is designed as a testing data set, and two indicators including root mean square error and relative accuracy are used to evaluate the imputation performance of the proposed method by comparison with some conventional methods (multiple linear regression, autoregressive integrated moving average model, and average historical method) by missing ratio. The applications in four scenarios demonstrate that the FCM-based imputation method outperforms conventional methods.