Improving the precision of an instrument is a hard task. Generally, this can be accomplished using stochastic filters, e.g., Kalman Filter (KF). Normally, these filters have to be tuned by an arbitrary definition of its noise covariance matrices. In this context, the objective of this paper is estimate dynamically the measurement noise covariance matrix, by means of the Bayes Theorem (BT), in a Kalman Filter algorithm, using the Inverted-Wishart distribution (IW-R Kalman Filter). The proposed method is compared with three other classic stochastic filters. In this comparison the IW-R Kalman Filter has achieved the best RMSE (Root Mean Square Error), outperforming the other filters. Therefore, one can conclude that dynamic estimation of noise covariances could improve the performance of the traditional stochastic filters.

Public on-street car parking is an important shared resource of a city infrastructure with a significant impact on traffic. This paper proposes a geostatistical model aimed to predict parking occupancy rates for different periods of the day. In the study case, the occupancy representation considers the georeferenced position of spots for a particular area of Los Angeles (USA). Different models are compared and their parameters are estimated using the available dataset of the parking area. The final model is chosen to generate a kriging map that helps to understand and predict the occupancy rates. The end goal is to open doors for modeling and predicting urban phenomenons with Geostatistics to help with planning public parking policies in high density urban areas.