Temporal and Spatial Models for Temperature Estimation Using Vehicle Data

Abstract: Safe driving is a topic of multiple factors where the road surface condition is one. Knowledge about the road status can for instance indicate whether it is risk for low friction and thereby help increase the safety in traffic. The ambient temperature is an important factor when determining the road surface condition and is therefore in focus. This work evaluates different methods of data fusion to estimate the ambient temperature at road segments. Data from vehicles are used during the temperature estimation process while measurements from weather stations are used for evaluation. Both temporal and spatial dependencies are examined through different models to predict how the temperature will evolve over time. The proposed Kalman filters are able to both interpolate in road segments where many observations are available and to extrapolate to road segments with no or only a few observations. The results show that interpolation leads to an average error of 0.5 degrees during winter when the temperature varies around five degrees day to night. Furthermore, the average error increases to two degrees during springtime when the temperature instead varies about fifteen degrees per day. It is shown that the risk of large estimation error is high when there are no observations from vehicles. As a separate result, it has been noted that the weather stations have a bias compared to the measurements from the cars.