Next Article in Journal
Moving Object Localization Based on UHF RFID Phase and Laser Clustering
Previous Article in Journal
Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(3), 824;

A Data Cleaning Method for Big Trace Data Using Movement Consistency

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Urban Design, Wuhan University, Wuhan 430070, China
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
Author to whom correspondence should be addressed.
Received: 31 January 2018 / Revised: 27 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [4605 KB, uploaded 9 March 2018]   |  


Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method. View Full-Text
Keywords: data cleaning; big data; vehicle trajectory; movement consistency modeling data cleaning; big data; vehicle trajectory; movement consistency modeling

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yang, X.; Tang, L.; Zhang, X.; Li, Q. A Data Cleaning Method for Big Trace Data Using Movement Consistency. Sensors 2018, 18, 824.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top