STRADA: Spatial-Temporal Dashboard for traffic forecasting

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

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 languageEnglish
Title of host publicationIEEE International Conference on Mobile Data Management (MDM)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages251-254
Number of pages4
ISBN (Electronic)9798350374551
DOIs
Publication statusPublished - 2024
Event25th IEEE International Conference on Mobile Data Management - Brussels, Belgium
Duration: 24 Jun 202427 Jun 2024

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
ISSN (Print)1551-6245

Conference

Conference25th IEEE International Conference on Mobile Data Management
Abbreviated titleMDM 2024
Country/TerritoryBelgium
CityBrussels
Period24/06/2427/06/24

Bibliographical note

Funding Information:
This work is funded by Innoviris within the research project TORRES.

Publisher Copyright:
© 2024 IEEE.

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