The Traffic Status and Pollutant Status Ontologies for the Smart City Domain

González-Mendoza, Miguel; Bermeo, Nestor Velasco; Orozco, Omar J. López

doi:10.1007/978-3-030-02840-4_8

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…To address this issue, few works have applied ontologies to improve smart cities environments ( Espinoza-Arias, Poveda-Villalón & Corcho, 2020 ; Mulero Martínez et al, 2018 ). For instance, Gonzalez-Mendoza, Velasco-Bermeo & López Orozco (2018) represented a pollution and traffic knowledge within a domain through ontologies. These ontologies classify things, actions, features based on traffic status and pollution status.…”

Section: Related Workmentioning

confidence: 99%

Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions

Ruiz de Gauna,

Sánchez,

Ruiz-Iniesta

2023

PeerJ Computer Science

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The environmental damage caused by air pollution has recently become the focus of city council policies. The concept of the green city has emerged as an urban solution by which to confront environmental challenges worldwide and is founded on air pollution levels that have increased meaningfully as a result of traffic in urban areas. Local governments are attempting to meet environmental challenges by developing public traffic policies such as air pollution protocols. However, several problems must still be solved, such as the need to link smart cars to these pollution protocols in order to find more optimal routes. We have, therefore, attempted to address this problem by conducting a study of local policies in the city of Madrid (Spain) with the aim of determining the importance of the vehicle routing problem (VRP), and the need to optimise a set of routes for a fleet. The results of this study have allowed us to propose a framework with which to dynamically implement traffic constraints. This framework consists of three main layers: the data layer, the prediction layer and the event generation layer. With regard to the data layer, a dataset has been generated from traffic data concerning the city of Madrid, and deep learning techniques have then been applied to this data. The results obtained show that there are interdependencies between several factors, such as weather conditions, air quality and the local event calendar, which have an impact on drivers’ behaviour. These interdependencies have allowed the development of an ontological model, together with an event generation system that can anticipate changes and dynamically restructure traffic restrictions in order to obtain a more efficient traffic system. This system has been validated using real data from the city of Madrid.

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Section: Related Workmentioning

confidence: 99%

Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions

Ruiz de Gauna,

Sánchez,

Ruiz-Iniesta

2023

PeerJ Computer Science

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“…Second, SO1 exchanged Primary knowledge with other SOs to get the ontology structure from the new words. It performed the steps as what previously described to expand knowledge from other SOs, and obtained the relationships as illustrated in Figure 17 (the shaded entity was adapted from a study to monitor traffic in the smart city domain [45] and the network (formed by the non-shaded entities) was adapted from a study in the health domain [113]).…”

Section: From Primary Knowledge Becoming Invented Knowledgementioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

192

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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The Traffic Status and Pollutant Status Ontologies for the Smart City Domain

Cited by 2 publications

References 9 publications

Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions

Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions

Machine learning and data analytics for the IoT

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