Comparing Different On-Street Parking Information for Parking Guidance and Information Systems

Parking search is a highly relevant problem in many cities. Parking Guidance and Information (PGI) systems support drivers by recommending locations and routes with higher chance to find parking. However, the relevance of such systems for on-street parking spaces is barely studied. In this paper, we investigate the consequences of providing the drivers with different levels of parking information to the search. Based on real on-street parking data, we investigated the scenario in which a driver does not find a parking space at the destination and has to decide on the next road to go, given three possible kinds of contextual information: (I) No parking information; (II) static information about the capacity of a road segment and (temporary) parking limitations; (III) real-time information collected from stationary sensors. Clearly the latter has strong implications in terms of deployment and operational costs. These scenarios lead to three different guidance strategies for a PGI system. We conducted empirical experiments on real data from San Francisco and on an artificially altered version of that dataset, to simulate a more competitive parking scenario. Results show that there is a significant reduction of parking search with more informed strategies, and that the use of realtime information offers only a limited improvement over static one. Only in presence of very limited parking availabilities, real-time data becomes more beneficial.