An Architecture for a Mobility Recommender System in Smart Cities

Abstract

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

Cyber Physical Recommender Systems for IoT Based Applications

“…A lot of research in the field of Intelligent Transportation Systems, or ITSs, is devoted to mitigate mobility problems, also by exploiting in novel ways information about the state of the road infrastructure [14]. Many commercial and academic ITSs provide optimal door-to-door routing exploiting multiple means of transport (i.e.…”

“…These problems have not only strong socio-economic implications, since the time spent in finding free parking spaces is a cause of stress and frustration for drivers, but there is also a dramatic ecological impact. From a technological stand, it is easy to envision a near future scenario of urban mobility, where it will be possible to know in real-time the availability of parking spaces for each off-street parking lot and on-street parking stall, thanks to Internet-of-Things (IoT) techniques [14]. By leveraging these new and potentially huge data sources, novel mobility applications become possible.…”

“…The objective of the MRS is to generate different ranked lists of possible multimodal door-to-door routes that include also parking spaces (either on-or off-street) by querying multiple times the Route Calculation Planner (RCP) with different parameters, in order to find the relevant alternative to propose to the drivers. Some previous research has been devoted to propose data-driven architectures for mobility [14].…”

“…Fundamental data are the one obtained by the public transportation network, and smart city policies and regulations. Starting from a user's query, which is composed by a starting location (typically the current location) and his/her destination, the Mobility Query Engine will reason on the available data to generate different queries for the RCP [14].…”

An overview of recommender systems in the internet of things

“…The authors of Cha et al (2016), Martino and Rossi (2016), and Yavari et al (2016) show how basic Artificial Intelligence (AI) approaches such as planning and clustering can be exploited for offering public services in a personalized fashion. They do not include basic recommendation approaches but, for example, scheduling and clustering that empower functionalities such as route planning and recommending points of interest.…”