Towards eXplainable AI for Mobility Data Science
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Abstract (arXiv)
This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI studies, argue the need for comprehensible explanations with human-centered approaches, and outline a research path toward XAI for Mobility Data Science.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 17 Jul 2023.
Builds on
6 of 25 references.
References · 6
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2019Attention is not Explanationcited by 916
- 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
- 2023Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectioncited by 14
- 2020Corpus-level and Concept-based Explanations for Interpretable Document Classificationcited by 4
- 2022Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classificationcited by 2
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Citing papers per year: 2023: 1.
1 paper.