Many location-based social networks (LBSNs) applications such as customized Point-Of-Interest (POI) recommendation, preference-based trip planning, travel time estimation, etc., involve an important task of understanding human trajectory patterns. In particular, identifying and linking trajectories to users who generate them – a problem called Trajectory-User Linking (TUL) – has become a focus of many recent works. TUL is usually studied as a multi-class classification problem and has gained recent attention because: (1) the number of labels/classes (i.e., users) is way larger than the number of motion patterns among various trajectories; and (2) the location-based trajectory data, especially the check-ins – i.e., events of reporting a location at particular Point of Interest (POI) with known semantics – are often extremely sparse. Towards addressing these challenges, we introduce a Trajectory Generative Adversarial Network (TGAN) as an approach to enable learning users motion patterns and location distribution, and to eventually identify human mobility. TGAN consists of two jointly trained neural networks, playing a Minimax game to (iteratively) optimize both components. The first one is the generator, learning trajectory representation by a Recurrent Neural Network (RNN) based model, aiming at fitting the underlying trajectory distribution of a particular individual and generate synthetic trajectories with intrinsic invariance and global coherence. The second one is the discriminator – a Convolutional Neural Network (CNN) based model that discriminates the generated trajectory from the real ones and provides guidance to train the generator model. We demonstrate that the above two models can be well tuned together to improve the TUL performance, while achieving superior accuracy when compared to existing approaches.

许多基于位置的社交网络 (LBSN) 应用程序，例如定制的兴趣点 (POI) 推荐、基于偏好的旅行规划、旅行时间估计等，都涉及了解人类轨迹模式的重要任务。特别是，识别轨迹并将其链接到生成它们的用户——一个称为轨迹-用户链接 (TUL) 的问题——已成为许多近期工作的焦点。TUL 通常作为一个多类分类问题来研究，并且最近受到关注，因为：（1）标签/类（即用户）的数量远大于各种轨迹之间的运动模式数量；(2) 基于位置的轨迹数据，尤其是签到——即在特定的兴趣点 (POI) 报告具有已知语义的位置的事件——通常非常稀疏。为了解决这些挑战，我们引入了轨迹生成对抗网络 (TGAN) 作为一种方法来学习用户的运动模式和位置​​分布，并最终识别人类的移动性。TGAN 由两个联合训练的神经网络组成，玩 Minimax 游戏以（迭代地）优化这两个组件。第一个是生成器，通过基于循环神经网络 (RNN) 的模型学习轨迹表示，旨在拟合特定个体的潜在轨迹分布并生成具有内在不变性和全局一致性的合成轨迹。第二个是鉴别器——一种基于卷积神经网络 (CNN) 的模型，可将生成的轨迹与真实轨迹区分开来，并为训练生成器模型提供指导。