TAQE: a data modeling framework for traffic and air quality applications in smart cities

Martínez, David ; Po, Laura ; Trillo-Lado, Raquel (Universidad de Zaragoza) ; Viqueira, José R. R.

Resumen: Air quality and traffic monitoring and prediction are critical problems in urban areas. Therefore, in the context of smart cities, many relevant conceptual models and ontologies have already been proposed. However, the lack of standardized solutions boost development costs and hinder data integration between different cities and with other application domains. This paper proposes a classification of existing models and ontologies related to Earth observation and modeling and smart cities in four levels of abstraction, which range from completely general-purpose
frameworks to application-specific solutions. Based on such classification and requirements extracted from a comprehensive set of state-of-the-art applications, TAQE, a new data modeling framework for air quality and traffic data, is defined. The effectiveness of TAQE is evaluated both by comparing its expressiveness with the state-of-the-art of the same application domain and by its application in the "TRAFAIR -Understanding traffic flows to improve air quality" EU project.

Idioma: Inglés

DOI: 10.1007/978-3-031-16663-1_3

Año: 2022

Publicado en: Lecture Notes in Computer Science 13403 (2022), 25-40
ISSN: 0302-9743

Factor impacto CITESCORE: 2.2 - Mathematics (Q2) - Computer Science (Q3)

Factor impacto SCIMAGO: 0.32 - Computer Science (miscellaneous) (Q3) - Theoretical Computer Science (Q4)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00
Financiación: info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)