Lovén L, Karsisto V, Järvinen H, Sillanpää MJ, Leppänen T, Peltonen E, et al. (2019) Mobile road weather sensor calibration by sensor fusion and linear mixed models. PLoS ONE 14(2): e0211702. https://doi.org/10.1371/journal.pone.0211702
Mobile road weather sensor calibration by sensor fusion and linear mixed models
|Author:||Lovén, Lauri1; Karsisto, Virve2; Järvinen, Heikki3;|
1Infotech Oulu, University of Oulu, Oulu, Finland
2Finnish Meteorological Institute, Helsinki, Finland
3University of Helsinki, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 2.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019042513232
Public Library of Science,
|Publish Date:|| 2019-04-25
Mobile, vehicle-installed road weather sensors are becoming ubiquitous. While mobile sensors are often capable of making observations on a high frequency, their reliability and accuracy may vary. Large-scale road weather observation and forecasting are still mostly based on stationary road weather stations (RWS). Though expensive, sparsely located and making observations on a relatively low frequency, RWS’ reliability and accuracy are well-known and accommodated for in the road weather forecasting models. Statistical analysis revealed that road weather conditions indeed have a great effect on how the observations of mobile and stationary road weather temperature sensors differ from each other. Consequently, we calibrated the observations of mobile sensors with a linear mixed model. The mixed model was fitted fusing ca. 20 000 pairs of mobile and RWS observations of the same location at the same time, following a rendezvous model of sensor calibration. The calibration nearly halved the MSE between the observations of the mobile and the RWS sensor types. Computationally very light, the calibration can be embedded directly in the sensors.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
112 Statistics and probability
213 Electronic, automation and communications engineering, electronics
The 6Genesis Flagship program is funded by the Academy of Finland (grant 318927). The program funding was awarded to the University of Oulu. The AI Enhanced Mobile Edge Computing project is funded by the Future Makers program of Jane and Aatos Erkko Foundation and the Technology Industries of Finland Centennial Foundation. The project funding was awarded to the University of Oulu. The Intelligent Arctic trucks project is funded by Regional Council of Lapland1 and European Regional Development Fund of the European Union2. The project funding was awarded to the Finnish Meteorological Institute. The Sod5G project is funded by Regional Council of Lapland and European Regional Development Fund of the European Union. The project funding was awarded to the Finnish Meteorological Institute. TheWiRMa (Industrial Internet Applications in Winter Road Maintenance) project is funded by the Regional Council of Lapland and the Interreg Nord fund of the European Union (http://www.interregnord.com/). The fund was awarded to the Finnish Meteorological Institute. The 5G-Safe project is funded by Business Finland (https://www.businessfinland.fi/en/). The fund was awarded to the Finnish Meteorological institute. Finally, V. Karsisto received a grant for PhD studies related to "Using car observations in road weather forecasting." The grant was awarded by the Finnish Cultural foundation (https://skr.fi/en). Grant number is 00150364. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2019 Lovén et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.