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.

翻译：在移动数据科学领域，对基于轨迹数据训练的模型进行解释并阐明实体时空运动这一复杂任务始终面临重大挑战。传统可解释人工智能技术虽潜力巨大，却常忽视轨迹数据中特有的结构与细微特征。针对这一不足，我们提出了一套整合关键XAI技术的综合框架：LIME（局部可解释模型无关解释）、SHAP（沙普利加性解释）、显著性图、注意力机制、直接轨迹可视化及排列特征重要性。与单独部署这些方法的传统策略不同，我们的统一方法充分利用各类技术的协同效能，为依赖轨迹数据的模型提供更深入、更细粒度的洞察。通过构建这一综合方法，我们有效应对了轨迹的多维本质，不仅显著增强了可解释性，更实现了对模型决策的语境化、深层次理解。为验证并优化该框架，我们开展了一项面向不同用户群体的偏好与接受度调查。研究结果揭示了显著差异：学术导向的专业人士（尤其是数据科学家、IT专家和机器学习工程师等角色）展现出深厚的技术理解力，且普遍偏好组合式可解释方法；而终端用户或对人工智能与数据科学了解有限的个体则更青睐简单工具，如显示时间步重要性的柱状图或标定船舶轨迹关键区段的可视化呈现。