Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020 |
| Editors | Chang-Tien Lu, Fusheng Wang, Goce Trajcevski, Yan Huang, Shawn Newsam, Li Xiong |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 409-412 |
| Number of pages | 4 |
| ISBN (electronic) | 9781450380195 |
| Publication status | Published - 3 Nov 2020 |
| Event | 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020 - Virtual, Online, United States Duration: 3 Nov 2020 → 6 Nov 2020 |
Publication series
| Name | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
|---|
Abstract
In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard (TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.
Keywords
- Dashboard, Spatial-Temporal Analytics, Traffic Analytics
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Information Systems
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Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020. ed. / Chang-Tien Lu; Fusheng Wang; Goce Trajcevski; Yan Huang; Shawn Newsam; Li Xiong. Association for Computing Machinery (ACM), 2020. p. 409-412 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - TA-Dash
T2 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
AU - Tempelmeier, Nicolas
AU - Sander, Anzumana
AU - Feuerhake, Udo
AU - Löhdefink, Martin
AU - Demidova, Elena
PY - 2020/11/3
Y1 - 2020/11/3
N2 - In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard (TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.
AB - In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard (TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.
KW - Dashboard
KW - Spatial-Temporal Analytics
KW - Traffic Analytics
UR - http://www.scopus.com/inward/record.url?scp=85097307057&partnerID=8YFLogxK
U2 - 10.1145/3397536.3422344
DO - 10.1145/3397536.3422344
M3 - Conference contribution
AN - SCOPUS:85097307057
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 409
EP - 412
BT - Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Trajcevski, Goce
A2 - Huang, Yan
A2 - Newsam, Shawn
A2 - Xiong, Li
PB - Association for Computing Machinery (ACM)
Y2 - 3 November 2020 through 6 November 2020
ER -