@article{10902/33537,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10902/33537},
abstract = {In the era of burgeoning data diversity in heterogeneous sources, unlocking valuable insights
becomes pivotal. Raw data often lack context and meaning, necessitating the deployment of
services that link and enhance data, thereby extracting meaningful patterns and information.
For example, exploring the significance of IoT sensors in measuring air quality across cities
emphasizes the potential to establish connections between air quality and associated metrics
like traffic intensity and meteorological conditions.
Introducing the Data Enrichment Toolchain (DET), this study underscores its role in
harmonizing and curating diverse datasets. DET operates on linked-data principles and adheres
to the NGSI-LD standard, enabling seamless integration and correlation analysis across disparate
data domains. The research delves into the intricate relationship between traffic patterns and
prevalent air pollutants, utilizing enriched datasets from European cities focusing on the smart
city of Madrid as a use-case.
Considering the COVID-19 pandemic?s impact on traffic flow and meteorological influences
on air quality, the study examines pre-pandemic, pandemic, and post-pandemic traffic scenarios
in Madrid. By leveraging DET-enhanced datasets, the investigation aims to unravel nuanced
insights into the interplay between traffic, meteorological factors, and air quality, offering
valuable implications for urban planning and pollution mitigation strategies.},
organization = {This work has been partially supported by the project SALTED (Situation-Aware Linked heterogeneous Enriched Data) from the European Union’s Connecting Europe Facility program under the Action Number 2020-EU-IA-0274, and by means of the project THROTTLE (TrustwortHy uRban mObility daTa markeTpLacE) under Grant Agreement No. TED2021-131988B-I00 funded by MCIN/AEI/10.13039/501100011033 and the European Union Next GenerationEU/PRTR.},
publisher = {Elsevier},
publisher = {Internet of Things, 2024, 26, 101232},
title = {Data enrichment toolchain: a use-case for correlation analysis of air quality, traffic, and meteorological metrics in Madrid's smart city},
author = {Jafari Tehrani, Amir Reza and González Carril, Víctor and Martín González, Laura and Sánchez González, Luis and Lanza Calderón, Jorge and Raza, Syed Mohsan and Alvi, Maira and Kaewnoparat, Kanawut and Minerva. Roberto and Crespi, Noel},
}