ASPIRE: An Agent-Oriented Smart Parking Recommendation System for Smart Cities

Rizvi, Syed R.; Zehra, Susan; Olariu, Stephan

doi:10.1109/mits.2018.2876569

Cited by 51 publications

(19 citation statements)

References 28 publications

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“…Another approach to smart parking is introduced in [67] with the creation of an Agent-Oriented Smart Parking Recommendation System (ASPIRE). In the ASPIRE system, users can set up their parking preferences, like maximum walking time, preferred location, etc, through a 'Local Agent' and the system will take all these data into consideration when a parking reservation request is made.…”

Section: Parkingmentioning

confidence: 99%

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

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Section: Parkingmentioning

confidence: 99%

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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“…With a specific end goal to customize suggestions, it is necessary to recognize details concerning every user. Client designs are depictions of clients' requirements, goals, inclinations, interests, and practices alongside clients' statistic qualities [11]. A few user demonstrating approaches have been proposed, from the run of the mill weighted vectors to area ontologies.…”

Section: User Modelingmentioning

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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“…(Dargaye, Gobin-Rahimbux, and Gooda Sahib-Kaudeer 2019) present an Agent Based Model of a parking system in Mauritius that uses independent agents to recommend parking spaces based on factors like expected arrival time, acceptable parking charges. (Rizvi, Zehra, and Olariu 2019) also employ a software broker agent that uses AHP to recommend parking spaces. (Hassan et al 2020) present a graph algorithm to increase the efficiency of automated parking systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying Systems Engineering framework for architecting a Smart Parking System within a Smart City

Joshi¹,

Mittal²,

Jayawant³

et al. 2021

INCOSE International Symp

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A Smart Parking System is an important feature in any Smart City, as it hopes to address a persistent challenge in urban mobility. Various technology‐based solutions have been developed. However, in such complex societal systems, it is important to consider various stakeholders and interfaces with other systems to choose an architecture that works efficiently for the given context. In this paper, we present the application of the Systems Engineering framework to develop a Smart Parking System architecture to meet the needs of a hypothetical Smart City. The framework shows the application of methods to analyse the context, prioritise needs, define and formally model alternate architectures, choose between the alternatives, and the use of a simulation model to validate requirements derived for the chosen architecture. Such systematic application of Systems Engineering methods can help planners and designers to negotiate complexities and avoid getting locked into sub‐optimal technological solutions.

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ASPIRE: An Agent-Oriented Smart Parking Recommendation System for Smart Cities

Cited by 51 publications

References 28 publications

A Review of Machine Learning and IoT in Smart Transportation

A Review of Machine Learning and IoT in Smart Transportation

Mobile recommender system based on smart city graph

Applying Systems Engineering framework for architecting a Smart Parking System within a Smart City

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