000165151 001__ 165151
000165151 005__ 20251219150014.0
000165151 0247_ $$2doi$$a10.1109/OJITS.2025.3637305
000165151 0248_ $$2sideral$$a146667
000165151 037__ $$aART-2025-146667
000165151 041__ $$aeng
000165151 100__ $$0(orcid)0009-0004-7119-3513$$aGómez, Iván
000165151 245__ $$aAdvanced prediction of traffic at different temporal scales using heterogeneous data sources
000165151 260__ $$c2025
000165151 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165151 5203_ $$aEfficient urban traffic management is a crucial challenge in modern smart cities, especially in densely populated areas with complex and dynamic traffic conditions. In this paper, we tackle the traffic prediction problem and present a lightweight architecture that combines sensor embeddings with dense layers, sustaining strong performance across both short- and long-term forecasting horizons while substantially reducing training time and enabling fast inference times. In comparative evaluations, our approach matches or surpasses the accuracy of more complex methods and consistently improves efficiency. To foster reproducibility, we release the code along with an enriched dataset that integrates traffic flows with contextual features such as weather conditions, temporal variables, and urban attributes. The richness and coverage of this dataset exceed those of existing public resources, enabling deeper and more comprehensive analyses of traffic dynamics. Overall, we demonstrate that a lightweight, well-designed architecture can achieve high performance and practical scalability for urban mobility management.
000165151 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T64-23R
000165151 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000165151 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165151 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri, Sergio$$uUniversidad de Zaragoza
000165151 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000165151 773__ $$g6 (2025), 1539-1550$$tIEEE open journal of intelligent transportation systems$$x2687-7813
000165151 8564_ $$s2284634$$uhttps://zaguan.unizar.es/record/165151/files/texto_completo.pdf$$yVersión publicada
000165151 8564_ $$s2809841$$uhttps://zaguan.unizar.es/record/165151/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165151 909CO $$ooai:zaguan.unizar.es:165151$$particulos$$pdriver
000165151 951__ $$a2025-12-19-14:58:24
000165151 980__ $$aARTICLE