VADITA: Visual Analytics for Detecting Illegal Transport Activities
This paper presents a visual analytics system designed to address the IEEE VAST Challenge 2024 Mini-Challenge 2. The system can support the matching and anomaly detection of multi-source heterogeneous spatio-temporal data, thereby enabling the detection of illegal transport activities. The primary contribution of the system lies in its analysis-driven interaction design.
Authors
- Yi Shan (ORCID: https://orcid.org/0000-0003-2646-8835)
- Zekai Shao (ORCID: https://orcid.org/0000-0003-2014-5293)
- Ailing Guo (ORCID: https://orcid.org/0000-0003-0529-141X)
- Xiao‐Bao Shu (ORCID: https://orcid.org/0000-0002-8256-5130)
- Qianhui Li (ORCID: https://orcid.org/0009-0000-7924-9842)
- Siming Chen (ORCID: https://orcid.org/0000-0002-2690-3588)
- Tian Qiu
Institutions
- Fudan University (CN)
Publication Details
- Published
- 2024-10-13
- DOI
- https://doi.org/10.1109/vastchallenge64683.2024.00007
- Citations
- 1
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.27