Advanced prediction of traffic at different temporal scales using heterogeneous data sources

Gómez, Iván ; Ilarri, Sergio (Universidad de Zaragoza)

Resumen: Efficient 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.

Idioma: Inglés

DOI: 10.1109/OJITS.2025.3637305

Año: 2025

Publicado en: IEEE open journal of intelligent transportation systems 6 (2025), 1539-1550
ISSN: 2687-7813

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-23R

Tipo y forma: Artículo (Versión definitiva)

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