Advanced Prediction of Traffic at Different Temporal Scales Using Heterogeneous Data Sources

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.

• Record URL:
  • https://doi.org/10.1109/OJITS.2025.3637305
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26877813
• Supplemental Notes:
  • © 2025 The Author(s). The contents of this paper reflect the views of the author[s] and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
• Authors:
  • Gómez, Iván
  • Ilarri, Sergio
• Publication Date: 2025

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1539-1550
• Serial:
  • IEEE Open Journal of Intelligent Transportation Systems
  • Volume: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2687-7813
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Predictive models; Sensors; System architecture; Time series; Traffic forecasting; Urban highways
• Geographic Terms: Madrid (Spain)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01980063
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 19 2026 10:53AM