Efficiently visualizing Spatial-Temporal traffic data plays an important role nowadays in traffic monitoring. Interactive dashboards offering effective visualizations of spatial-temporal traffic data play a more prominent role in traffic monitoring. In this paper, we introduce a dashboard for visualizing traffic data. Specifically, our dashboard integrates spatial-temporal components for the time-series traffic data of Brussels, which is the first GNN-based traffic demonstration tool for Brussels. Furthermore, we provide an interface for displaying traffic prediction of deep-learning-based Spatial-Temporal Graph Neural Networks (STGNNs), which have demonstrated state of the art performance in Intelligent Transpiration Systems (ITS). In addition, we demonstrate two real-world use cases by using the proposed dashboard which provides the potential for a future tool to achieve intelligent transportation management.
Moghadas, SM, Yangxintong, L, Cornelis, B & Munteanu, A 2024, STRADA: Spatial-Temporal Dashboard for traffic forecasting. in IEEE International Conference on Mobile Data Management (MDM). Proceedings - IEEE International Conference on Mobile Data Management, Institute of Electrical and Electronics Engineers Inc., pp. 251-254, 25th IEEE International Conference on Mobile Data Management, Brussels, Belgium, 24/06/24. https://doi.org/10.1109/MDM61037.2024.00052
Moghadas, S. M., Yangxintong, L., Cornelis, B., & Munteanu, A. (2024). STRADA: Spatial-Temporal Dashboard for traffic forecasting. In IEEE International Conference on Mobile Data Management (MDM) (pp. 251-254). (Proceedings - IEEE International Conference on Mobile Data Management). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MDM61037.2024.00052
@inproceedings{5467255e193946299376055a4ec6dbe3,
title = "STRADA: Spatial-Temporal Dashboard for traffic forecasting",
abstract = "Efficiently visualizing Spatial-Temporal traffic data plays an important role nowadays in traffic monitoring. Interactive dashboards offering effective visualizations of spatial-temporal traffic data play a more prominent role in traffic monitoring. In this paper, we introduce a dashboard for visualizing traffic data. Specifically, our dashboard integrates spatial-temporal components for the time-series traffic data of Brussels, which is the first GNN-based traffic demonstration tool for Brussels. Furthermore, we provide an interface for displaying traffic prediction of deep-learning-based Spatial-Temporal Graph Neural Networks (STGNNs), which have demonstrated state of the art performance in Intelligent Transpiration Systems (ITS). In addition, we demonstrate two real-world use cases by using the proposed dashboard which provides the potential for a future tool to achieve intelligent transportation management.",
author = "Moghadas, {Seyed Mohamad} and Lyu Yangxintong and Bruno Cornelis and Adrian Munteanu",
note = "Funding Information: This work is funded by Innoviris within the research project TORRES. Publisher Copyright: {\textcopyright} 2024 IEEE.; 25th IEEE International Conference on Mobile Data Management, MDM 2024 ; Conference date: 24-06-2024 Through 27-06-2024",
year = "2024",
doi = "10.1109/MDM61037.2024.00052",
language = "English",
series = "Proceedings - IEEE International Conference on Mobile Data Management",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "251--254",
booktitle = "IEEE International Conference on Mobile Data Management (MDM)",
address = "United States",
}