A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. (February 2015)

• Record Type: Journal Article
• Authors: Tang, Jinjun; Zhang, Guohui; Wang, Yinhai; Wang, Hua; Liu, Fang
• Is Part Of: Transportation research. Volume 51(2015)
• Page Start: 29
• Page End: 40
• Publication Date: 2015-02
• Subjects: Missing sensor data -- Fuzzy C-means -- Genetic algorithm -- Imputation -- Traffic volume; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2014.11.003
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We use the FCM based method to estimate the missing traffic data. Genetic algorithm is applied to complete optimization process. Four types of data collect in different intervals are used to testify the method. The testing results prove the superiority of the FCM method. Abstract: Although various innovative traffic sensing technologies have been widely employed, incomplete sensor data is one of the most major problems to significantly degrade traffic data quality and integrity. In this study, a hybrid approach integrating the Fuzzy C-Means (FCM)-based imputation method with the Genetic Algorithm (GA) is develop for missing traffic volume data estimation based on inductance loop detector outputs. By utilizing the weekly similarity among data, the conventional vector-based data structure is firstly transformed into the matrix-based data pattern. Then, the GA is applied to optimize the membership functions and centroids in the FCM model. The experimental tests are conducted to verify the effectiveness of the proposed approach. The traffic volume data collected at different temporal scales were used as the testing dataset, and three different indicators, including root mean square error, correlation coefficient, and relative accuracy, are utilized to quantify the imputation performance compared with some conventional methods (Historical method, Double Exponential Smoothing, and Autoregressive Integrated Moving Average model). The results show the proposed approach outperforms the conventional methods under prevailing traffic conditions.