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.
| Original language | English |
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| Title of host publication | IEEE International Conference on Mobile Data Management (MDM) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 251-254 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798350374551 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 25th IEEE International Conference on Mobile Data Management - Brussels, Belgium Duration: 24 Jun 2024 → 27 Jun 2024 |
Publication series
| Name | Proceedings - IEEE International Conference on Mobile Data Management |
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| ISSN (Print) | 1551-6245 |
Conference
| Conference | 25th IEEE International Conference on Mobile Data Management |
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| Abbreviated title | MDM 2024 |
| Country/Territory | Belgium |
| City | Brussels |
| Period | 24/06/24 → 27/06/24 |
Bibliographical note
Funding Information:This work is funded by Innoviris within the research project TORRES.
Publisher Copyright:
© 2024 IEEE.