Next Article in Journal
Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Next Article in Special Issue
An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments
Previous Article in Journal
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(10), 2160; https://doi.org/10.3390/s17102160

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

1
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China
2
Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
3
School of Computer Science and Engineering, the State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 24 July 2017 / Revised: 11 September 2017 / Accepted: 16 September 2017 / Published: 21 September 2017
(This article belongs to the Special Issue Sensor Networks for Smart Roads)
View Full-Text   |   Download PDF [1298 KB, uploaded 21 September 2017]   |  

Abstract

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. View Full-Text
Keywords: traffic flow imputation; spatial interpolation; spatial correlation; marginal distribution; copula model traffic flow imputation; spatial interpolation; spatial correlation; marginal distribution; copula model
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ma, X.; Luan, S.; Du, B.; Yu, B. Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors. Sensors 2017, 17, 2160.

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