On-Line Filtering of On-Street Parking Data to Improve Availability Predictions

Knowing where to park in advance is a most wished feature by many drivers. In recent years, many research efforts have been spent to analyse massive amount of parking information, to learn availability trends and thus to predict, within a Parking Guidance and Information (PGI) system, where there is the highest chance to find free parking spaces. The most of these solutions exploits raw data coming from stationary sensors or crowd-sensed by mobile probes. In both the cases, these massive amounts of data present a high level of noise, which heavily affects the quality of availability predictions. In a previous work we demonstrated that a 2-step approach, based on machine learning techniques to filter out noise, improves the quality of parking availability predictions over raw data. In this paper we propose a further advancement of that approach, by including a technique to perform such noise filtering in real-time, with reduced computational efforts. The proposal has been empirically tested on a real-world dataset of on-street parking information from the SFpark project, and compared against a regression model based on SVR, to perform parking availability predictions. Results show that the predictions obtained with the new on-line approach show a better balance between average and entropy in errors distribution with respect to the use of raw data coming from the sensors.