An Architecture for a Mobility Recommender System in Smart Cities

To date, there is a wide availability of academic and commercial ICT proposals to improve urban mobility. Nevertheless, in the literature, there is still a lack of suitable solutions for door-to-door routing supporting users from their origins to destinations and including the suggestion on where to park. On the other hand, in an Internet-of-Things (IoT) scenario, a lot of novel information sources could be exploited to compute more efficient mobility solutions to be proposed to the user. As an example, parking availability data could be easily collected and exploited to provide multimodal routes (i.e. routes with at least two different means of transportation) that include suggestions on where to park and how to reach the final destination. In this paper, we describe a distributed IoT architecture towards the definition of a Mobility Recommender System. In particular, we focus on a car-based multimodality, where the user always starts a trip with his/her private vehicle, but he/she can also leave the car in Park-and-Ride infrastructures and reach the destination with public transportation. This type of routing on a wider search area will result to be more costly, and thus, it will particularly benefit from a parallel computational architectural solution.