An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data.

Junsheng Huang,Tong Zhang,Changjun Miao,Yun Bai,Baohua Mao

doi:10.3390/s20071992

Junsheng Huang, Tong Zhang + Show 3 more

Open Access

https://doi.org/10.3390/s20071992

Copy DOI

Abstract

Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.

Highlights

With the advent of the intelligent transportation era, various traffic sensors, including loop detectors, cameras, and global positioning system (GPS) receivers, have been widely adopted to facilitate traffic control and management
Motivated by recent works in the literature [16], this paper aims to establish a novel imputation method integrating the matrix-based method, the fuzzy C-means (FCM) method, and the genetic algorithm (GA). (1) It should be noted that taxi demand volume data on weekdays or weekends in the taxi GPS datasets share periodic similarity spatiotemporally
Troyanskaya et al [39] proposed the weight k-nearest neighbor imputation (WKNNI) to estimate the missing values, and the estimation results showed that the imputation performance of the WKNNI was more robust than that of the original KNNI

Summary

Introduction

With the advent of the intelligent transportation era, various traffic sensors, including loop detectors, cameras, and GPS receivers, have been widely adopted to facilitate traffic control and management. Taking GPS as an example, as seen, it consists of three parts, including the space segment, the user segment, and the control segment. The user segment consists of GPS receivers and the user community. Almost all taxis are required to be equipped with GPS receivers for location recording. A substantial amount of taxi mobile data from taxis is accurately recorded in the taxi GPS datasets. The passengers’ pick-up and drop-off locations, dates, etc., are obtained as they are recorded in the taxi GPS datasets

Objectives

Methods

Conclusion