Towards a smart city ontology

Abid, Tarek; Zarzour, Hafed; Laouar, Mohamed Ridda; Khadir, Mohamed Tarek

doi:10.1109/aiccsa.2016.7945823

Cited by 13 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, in this paper, we develop the concept of LUCO and RNN, which can be considered in future studies of smart cities. Such an ontology and deep learning are beneficial for both citizens and city planners as it allows interoperability among different land use and transportation frameworks [57].…”

Section: Discussionmentioning

confidence: 99%

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Azad

Xin

2021

IJGI

View full text Add to dashboard Cite

Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient.

show abstract

Section: Discussionmentioning

confidence: 99%

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Azad

Xin

2021

IJGI

View full text Add to dashboard Cite

show abstract

“…This Smart City context is characterized by data collected from various distributed systems. Purposing these task in [14] the so called Semantic Web is used for designing a new Smart City ontology. The primary focus is to address the interoperability among the different systems and frameworks for describing Smart City objects.…”

Section: Philipp Skowronmentioning

confidence: 99%

“…In [17] this description is extended for IoTbased applications. Nevertheless, [14], [15], [16] and [17] all have a strong technical focus and do not mind the user-centered perspective.…”

Section: Philipp Skowronmentioning

confidence: 99%

Smart Urban Design Space

Skowron¹,

Aleithe²,

Wallrafen³

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

The irreversible process of demographic change, especially in Germany, leads to numerous challenges. According to this, research has to face the task to integrate the constantly ageing population into the urban and public space in such a way that there are as few barriers as possible. With the support of digitalization, so-called smart urban objects are being designed in order to do make integration, so that people and the available technology can be used most efficiently. A special ontology has been developed to meet this demand.

show abstract

“…Actually, many recent studies (Poveda-Villalón et al, 2014;Komninos et al, 2015;Abid et al,2016;Ranietal.,2016;etc. )claimthatontologiesandontology-basedtechnologiescanfind widespreadapplicationanduseinthefieldofsmartcities,whiletheyhavegraduallybeguntopenetrate thisscientificareaandhavebecomeanewandpromising(intermsofprospectsandresults)research topic.Anontology,definedas"... a formal, explicit specification of a shared conceptualization" (Studeretal.,1998,p.184),meaningaformaldescription/representationofknowledgeofaspecific scientificfield,providestheessentialconceptstobemodeled,aswellastherelationshipsamong them.…”

Section: Introductionmentioning

confidence: 99%

Conceptualizing Small and Medium-Sized Smart Cities in the Mediterranean Region

Παναγιωτοπούλου

Kokla

Stratigea

2019

International Journal of E-Planning Research

View full text Add to dashboard Cite

Despite the remarkable interest in smart cities, noticed during the last decade, a consistent comprehension of the concept is not yet fully realized. Various definitions, ranging from exclusively technology-oriented perceptions to broader views, have been introduced, establishing a definitional polyphony and causing lack of semantic interoperability. Empirical evidence witnesses the prevalence of technology-pushed smart city initiatives as well as their failure to meet expectations in several urban domains. When planning “smart,” the relevance of ICT and their applications should be in alignment with spatial and other urban peculiarities and sub-systems' interactions, implying the need for getting deep insight into the city's ontology. The paper focuses on the extension/enrichment of an existing smart city ontology, with concepts and relationships stemming from Mediterranean small and medium-sized cities, in an attempt to outline their main key drivers and their interrelationships and fully grasp the smart city concept in the particular spatial context.

show abstract

Towards a smart city ontology

Cited by 13 publications

References 10 publications

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Smart Urban Design Space

Conceptualizing Small and Medium-Sized Smart Cities in the Mediterranean Region

Contact Info

Product

Resources

About