Evaluating the Social Benefit of a Negotiation–Based Parking Allocation

Barile, Francesco; Napoli, Claudia Di; Nocera, Dario Di; Rossi, Silvia

doi:10.1007/978-3-319-18944-4_2

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Nevertheless, up to now extremely few investigations have been carried out on routing consisting of vehicle parking [19]. Park guidance and information (PGI) remedies are developed to increase the likelihood of finding a parking area [20], however without thinking about the opportunity to find a better remedy for the vehicle driver (e.g., a remedy that is not near the destination but less costly) [21], [22]. Furthermore, PGI remedies leave the burden of making the vehicle parking decision on the vehicle drivers by assessing all the available details in the destination area [23].…”

Section: Graph Modelmentioning

confidence: 99%

Mobile recommender system based on smart city graph

Khoshnaw

Shwany

Mustafa

et al. 2022

IJEECS

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<span>Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.</span>

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Section: Graph Modelmentioning

confidence: 99%

Mobile recommender system based on smart city graph

Khoshnaw

Shwany

Mustafa

et al. 2022

IJEECS

View full text Add to dashboard Cite

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City Parking Allocations as a Bundle of Society-Aware Deals

Barile

Bove

Napoli

et al. 2017

Understanding Complex Systems

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An Architecture for a Mobility Recommender System in Smart Cities

Martino

Rossi

2016

Procedia Computer Science

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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

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Evaluating the Social Benefit of a Negotiation–Based Parking Allocation

Cited by 4 publications

References 5 publications

Mobile recommender system based on smart city graph

Mobile recommender system based on smart city graph

City Parking Allocations as a Bundle of Society-Aware Deals

An Architecture for a Mobility Recommender System in Smart Cities

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