论文标题
与时空效率约束的轨迹产生的分解深层生成模型
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints
论文作者
论文摘要
轨迹数据生成是一个重要的领域，它表征了移动性数据的生成过程。传统方法在很大程度上依赖于预定的启发式方法和分布，并且在学习未知机制方面弱。受深度生成神经网络在图像和文本中的成功启发，一个快速发展的研究主题是轨迹数据的深层生成模型，可以为复杂的潜在模式学习表达的解释模型。对于许多应用，这是一个新生但有希望的领域。我们首先提出了新型的深层生成模型，分别将分别表征全球语义和局部语义的时间变化和时变量的潜在变量分解。然后，我们基于变异推理和限制优化制定了新的推理策略，以封装时空有效性。已经开发了新的深神经网络体系结构，以实施具有新将来的潜在可变先验的推理和生成模型。所提出的方法在广泛的实验中实现了定量和定性评估的显着改善。
Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distributions and are weak in learning unknown mechanisms. Inspired by the success of deep generative neural networks for images and texts, a fast-developing research topic is deep generative models for trajectory data which can learn expressively explanatory models for sophisticated latent patterns. This is a nascent yet promising domain for many applications. We first propose novel deep generative models factorizing time-variant and time-invariant latent variables that characterize global and local semantics, respectively. We then develop new inference strategies based on variational inference and constrained optimization to encapsulate the spatiotemporal validity. New deep neural network architectures have been developed to implement the inference and generation models with newly-generalized latent variable priors. The proposed methods achieved significant improvements in quantitative and qualitative evaluations in extensive experiments.
