000145050 001__ 145050
000145050 005__ 20251219150011.0
000145050 0247_ $$2doi$$a10.1016/j.dib.2024.110878
000145050 0248_ $$2sideral$$a139904
000145050 037__ $$aART-2024-139904
000145050 041__ $$aeng
000145050 100__ $$aGómez, Iván
000145050 245__ $$aEnriched traffic datasets for the city of Madrid: Integrating data from traffic sensors, the road infrastructure, calendar data and weather data
000145050 260__ $$c2024
000145050 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145050 5203_ $$aThe proliferation of urban areas and the concurrent increase in vehicular mobility have escalated the urgency for advanced traffic management solutions. This data article introduces two traffic datasets from Madrid, collected between June 2022 and February 2024, to address the challenges of traffic management in urban areas. The first dataset provides detailed traffic flow measurements (vehicles per hour) from urban sensors and road networks, enriched with weather data, calendar data and road infrastructure details from OpenStreetMap. This combination allows for an in-depth analysis of urban mobility. Through preprocessing, data quality is ensured by eliminating inconsistent sensor readings. The second dataset is enhanced for advanced predictive modelling. It includes time-based transformations and a tailored preprocessing pipeline that standardizes numeric data, applies one-hot encoding to categorical features, and uses ordinal encoding for specific features. In constructing the datasets, we initially employed the k-means algorithm to cluster data from multiple sensors, thereby highlighting the most representative ones. This clustering can be adapted or modified according to the user's needs, ensuring flexibility for various analyses and applications.
This work underscores the importance of advanced datasets in urban planning and highlights the versatility of these resources for multiple practical applications. We highlight the relevance of the collected data for a variety of essential purposes, including traffic prediction, infrastructure planning, studies on the environmental impact of traffic, event planning, and conducting simulations. These datasets not only provide a solid foundation for academic research but also for designing and implementing more effective and sustainable traffic policies. Furthermore, all related datasets, source code, and documentation have been made publicly available, encouraging further research and practical applications in traffic management and urban planning.
000145050 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T64-23R
000145050 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000145050 592__ $$a0.198$$b2024
000145050 593__ $$aMultidisciplinary$$c2024$$dQ3
000145050 593__ $$aEducation$$c2024$$dQ4
000145050 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145050 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri, Sergio$$uUniversidad de Zaragoza
000145050 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000145050 773__ $$g57 (2024), 110878[13 pp.]$$pData brief$$tData in Brief$$x2352-3409
000145050 787__ $$tEnriched Traffic Datasets for Madrid$$whttps://data.mendeley.com/datasets/697ht4f65b/1
000145050 8564_ $$s1643956$$uhttps://zaguan.unizar.es/record/145050/files/texto_completo.pdf$$yVersión publicada
000145050 8564_ $$s1360598$$uhttps://zaguan.unizar.es/record/145050/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145050 909CO $$ooai:zaguan.unizar.es:145050$$particulos$$pdriver
000145050 951__ $$a2025-12-19-14:58:18
000145050 980__ $$aARTICLE