In the domain of Mobility Data Science, the intricate task of interpreting
models trained on trajectory data, and elucidating the spatio-temporal movement
of entities, has persistently posed significant challenges. Conventional XAI
techniques, although brimming with potential, frequently overlook the distinct
structure and nuances inherent within trajectory data. Observing this
deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI
techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP
(SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct
trajectory visualization, and Permutation Feature Importance (PFI). Unlike
conventional strategies that deploy these methods singularly, our unified
approach capitalizes on the collective efficacy of these techniques, yielding
deeper and more granular insights for models reliant on trajectory data. In
crafting this synthesis, we effectively address the multifaceted essence of
trajectories, achieving not only amplified interpretability but also a nuanced,
contextually rich comprehension of model decisions. To validate and enhance our
framework, we undertook a survey to gauge preferences and reception among
various user demographics. Our findings underscored a dichotomy: professionals
with academic orientations, particularly those in roles like Data Scientist, IT
Expert, and ML Engineer, showcased a profound, technical understanding and
often exhibited a predilection for amalgamated methods for interpretability.
Conversely, end-users or individuals less acquainted with AI and Data Science
showcased simpler inclinations, such as bar plots indicating timestep
significance or visual depictions pinpointing pivotal segments of a vessel's
trajectory.

在移动数据科学领域中，本文综合了关键的可解释性技术，如 LIME、SHAP、saliency maps、attention mechanisms、direct trajectory visualization 和 PFI，来揭示轨迹数据模型的空间 - 时间运动，并调查了用户偏好。

提高移动数据科学中的可解释性：方法结合

Enhancing Explainability in Mobility Data Science through a combination  of methods

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.

本文介绍了我们关于可解释的移动数据科学 XAI 方案的持续研究成果，重点是通过使用时空图神经网络和反事实方法从密集轨迹数据中学习可解释模型，如车辆和船舶的 GPS 轨迹，并回顾了现有的 GeoXAI 研究，主张采用以人为本的方法进行全面的解释，并概述了通向移动数据科学的 XAI 研究路径。