人类移动性预测的通用模型
A Universal Model for Human Mobility Prediction
AI总结：
提出通用人类移动性预测模型UniMob，通过多视图移动分词器和扩散Transformer架构统一处理个体轨迹与人群流量，并利用双向对齐机制实现相互增强，在噪声和稀缺数据场景下性能显著提升。
摘要：
预测人类移动性对城市规划、交通控制和应急响应至关重要。移动行为可分为个体和集体两类，这些行为由多样的移动数据记录，如个体轨迹和人群流量。作为移动数据的不同模态，个体轨迹和人群流量具有紧密的耦合关系。人群流量源于个体轨迹的自下而上聚合，而人群流量施加的约束又塑造了这些个体轨迹。由于个体轨迹和人群流量之间的模态鸿沟，现有移动预测方法局限于单一任务。在本工作中，我们旨在统一移动预测以突破特定任务模型的局限。我们提出了一种通用人类移动性预测模型（命名为 UniMob），可同时应用于个体轨迹和人群流量。UniMob 利用多视图移动分词器，将轨迹和流量数据转换为时空 token，通过扩散 Transformer 架构实现统一的序列建模。为弥合这两种数据模态不同特征之间的差距，我们实现了一种新颖的双向个体与集体对齐机制。该机制能够从不同移动数据中学习共同的时空模式，促进轨迹和流量预测的相互增强。在真实世界数据集上的大量实验验证了我们的模型在轨迹和流量预测上优于最先进的基线方法。尤其在有噪声和稀缺数据场景下，我们的模型在 MAPE 和 Accuracy@5 上实现了最高超过 14% 和 25% 的性能提升。
英文摘要：
Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility data, such as individual trajectory and crowd flow. As different modalities of mobility data, individual trajectory and crowd flow have a close coupling relationship. Crowd flows originate from the bottom-up aggregation of individual trajectories, while the constraints imposed by crowd flows shape these individual trajectories. Existing mobility prediction methods are limited to single tasks due to modal gaps between individual trajectory and crowd flow. In this work, we aim to unify mobility prediction to break through the limitations of task-specific models. We propose a universal human mobility prediction model (named UniMob), which can be applied to both individual trajectory and crowd flow. UniMob leverages a multi-view mobility tokenizer that transforms both trajectory and flow data into spatiotemporal tokens, facilitating unified sequential modeling through a diffusion transformer architecture. To bridge the gap between the different characteristics of these two data modalities, we implement a novel bidirectional individual and collective alignment mechanism. This mechanism enables learning common spatiotemporal patterns from different mobility data, facilitating mutual enhancement of both trajectory and flow predictions. Extensive experiments on real-world datasets validate the superiority of our model over state-of-the-art baselines in trajectory and flow prediction. Especially in noisy and scarce data scenarios, our model achieves the highest performance improvement of more than 14% and 25% in MAPE and Accuracy@5.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
