Publicação
Leveraging IoT and Intelligent Automation for Enhanced Traffic Flow in Urban Areas
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Neves, Maria de Fátima dos Santos Trindade | |
| dc.contributor.author | Gregório, Miguel Bruno Mendonça | |
| dc.date.accessioned | 2025-11-17T12:26:28Z | |
| dc.date.embargo | 2027-10-31 | |
| dc.date.issued | 2025-10-31 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | Urban traffic congestion poses significant environmental, economic, and social challenges that require innovative and scalable solutions. This study presents a novel integration of Internet of Things (IoT) technologies and intelligent automation to optimize traffic flow in urban areas, using London’s A40 Westway corridor as a case study. Leveraging open data from Transport for London (TfL) and meteorological sources, a real-time traffic monitoring and forecasting framework was built with Microsoft Power Platform tools, including Power Automate, AI Builder, and Power Apps. Disruption data is collected at 30-minute intervals and enriched with contextual weather information, such as temperature, precipitation, and wind speed, retrieved from the Open-Meteo API. The resulting dataset trained a predictive model, achieving 83.3% accuracy in short-term traffic severity forecasting. Embedded within an interactive application, the system enables traffic operators to visualise current disruptions and request forecasts, supported by automated data ingestion and logging workflows. Despite limitations in historical data availability, this research demonstrates the effectiveness of low-code tools in delivering scalable, replicable traffic management solutions grounded in real-world constraints. It advances the field of smart mobility by illustrating how open data, predictive modeling, and low-code automation can be combined to support intelligent urban systems, presenting a replicable and adaptable framework that cities can adopt to optimize traffic flow through data-driven and automated decision-making. | pt_PT |
| dc.identifier.tid | 204072697 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190851 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Urban Traffic Management | pt_PT |
| dc.subject | Intelligent Automation | pt_PT |
| dc.subject | Internet of Things | pt_PT |
| dc.subject | Smart Mobility | pt_PT |
| dc.subject | Predictive Modeling | pt_PT |
| dc.subject | Low-Code | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.subject | SDG 13 - Climate action | pt_PT |
| dc.subject | SDG 17 - Partnerships for the goals | pt_PT |
| dc.title | Leveraging IoT and Intelligent Automation for Enhanced Traffic Flow in Urban Areas | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.embargofct | A tese vai ser publicada como paper num tier-1 journal. | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Business Intelligence | pt_PT |