Next Article in Journal
Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path
Next Article in Special Issue
Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG
Previous Article in Journal
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time
Previous Article in Special Issue
Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 342; doi:10.3390/s17020342

A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet

1
College of Physics & Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China
2
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
3
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 25 November 2016 / Revised: 18 January 2017 / Accepted: 4 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Sensors for Transportation)

Abstract

This paper presents a novel framework for trajectories’ extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we focus on feature construction and parameter selection for the fuzzy C-means clustering method. Following the clustering procedure, the trajectory cleaning algorithm is implemented based on a new introduced fuzzy connecting matrix, which evaluates the possibility of data belonging to the same trajectory and helps detect the anomalies in a ranked context-related order. Finally, the trajectory connecting algorithm is proposed to solve the issue that occurs in some cases when a route trajectory is incorrectly partitioned into several clusters. In the missing data recovery procedure, we developed the contextual linear interpolation for the cases of missing data occurring inside the trajectory and the median value interpolation for the cases of missing data outside the trajectory. Extensive experiments are conducted to demonstrate that the proposed framework offers a powerful ability to extract and recovery bus trajectories sampled from the Internet. View Full-Text
Keywords: trajectory processing; anomaly detection; missing data recovery; clustering trajectory processing; anomaly detection; missing data recovery; clustering
Figures

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).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Tong, C.; Chen, H.; Xuan, Q.; Yang, X. A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet. Sensors 2017, 17, 342.

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

1

Comments

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