Abstract
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.
Highlights
Mobile, vehicle-installed sensors and road weather station (RWS) networks can together provide denser and higher quality information than either alone
This study presents a novel, sensor fusion based method to calibrate mobile surface temperature sensors to agree with RWSs
This study analyzed the observations of mobile (Teconer RCM411, RTS411) and RWS (Vaisala DRS511, DST111, DSC111) road weather sensors and identified conditions and factors that affect how their measurements differ from each other
Summary
Vehicle-installed sensors and road weather station (RWS) networks can together provide denser and higher quality information than either alone. They can support optimization of maintenance operations, such as snow clearance and prevention of slipperiness, and generation of real-time warnings for road users. Improved technologies and increased availability of mobile observations can drastically improve the coverage and quality of observations on roads. The amount of available mobile observations have recently considerably increased. During November 2016—March 2017, for instance, vehicles fitted with Teconer Oy’s optical sensors (RCM411 and RTS411) covered globally approximately 200 000 km of roads per month observing friction, surface water deposits, and road surface temperature [1]
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