Learning the relevance of parking information in VANETs

The use of Vehicular Ad-Hoc Network (VANET) has been applied to many applications involving information dissemination. Many of such applications are limited by the communication limitations of a VANET, such as limited transmission range and bandwidth. This imposes a necessity for evaluating the relevance of information. This paper proposes the use of machine learning for finding relevance of information for a parking information dissemination system. The proposed method uses the learned relevance for aiding vehicles in decision making by finding the probability that a given parking location will be available at the time of arrival. The method was evaluated through simulations and the results show that the proposed method is successful at learning the relevance of parking reports, which resulted in lower parking discovery times for vehicles.