Predicting the next pickup location of individual users is a fundamental problem in intelligent mobility systems, which requires modeling personalized travel behaviors under complex spatiotemporal contexts. Existing methods mainly learn sequential dependencies from raw trajectories, but often fail to capture high-level behavioral semantics and to effectively disentangle long-term habitual preferences from short-term contextual intentions. In this paper, we propose a semantic embedding based dual stream spatiotemporal attention model for next pickup location prediction. Raw trajectories are first transformed into semantically enriched activity sequences to encode users' stay behaviors and movement semantics. A dual stream architecture is then designed to explicitly decouple long-term historical patterns and short-term dynamic intentions, where each stream employs spatiotemporal attention mechanisms to model dependencies at different temporal scales. To integrate heterogeneous contextual information, a context aware dynamic fusion module adaptively balances the contributions of the two streams. Finally, an attention based matching strategy is used to predict the probability distribution over candidate pickup locations. Experiments on real world ride hailing datasets demonstrate that the proposed model consistently outperforms state of the art methods, validating the effectiveness of semantic trajectory abstraction and dual stream spatiotemporal attention for individualized mobility behavior modeling.

翻译：预测个体用户的下一个上车位置是智能出行系统中的核心问题，这需要在复杂时空背景下对个性化出行行为进行建模。现有方法主要从原始轨迹中学习序列依赖关系，但往往难以捕获高层次的行为语义，且未能有效分离长期习惯性偏好与短期情境意图。本文提出一种基于语义嵌入的双流时空注意力模型用于下一上车位置预测。首先将原始轨迹转化为语义增强的活动序列，以编码用户的停留行为与移动语义；随后设计双流架构显式解耦长期历史模式与短期动态意图，其中每个流采用时空注意力机制建模不同时间尺度上的依赖关系。为整合异构上下文信息，一个情境感知的动态融合模块自适应地平衡双流的贡献。最后，基于注意力的匹配策略被用于预测候选上车位置的概率分布。在真实网约车数据集上的实验表明，所提模型持续优于现有最优方法，验证了语义轨迹抽象与双流时空注意力对个体化移动行为建模的有效性。