Factorized deep generative models for end-to-end trajectory generation with spatiotemporal validity constraints
Factorized deep generative models for end-to-end trajectory generation with spatiotemporal validity constraints
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DOI：
10.1145/3557915.3560994
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Liming Zhang;Liang Zhao;D. Pfoser
中科院分区：
文献类型：
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作者：
Liming Zhang;Liang Zhao;D. Pfoser
越来越多的研究领域，如基于位置的社交网络，智能交通系统和城市计算利用大量的轨迹数据的基准数据管理方法和分析方法。鉴于可用的大型数据集的普遍缺乏，真实的合成轨迹数据集变得重要。这项工作提出了轨迹数据的深度生成模型，可以学习复杂潜在模式的解纠缠模型。现有的方法依赖于预定义的算法，不能学习未知的潜在生成机制。所提出的新型深度生成类VAE模型分解了全局和局部语义（习惯与随机路由变化）。我们进一步开发新的推理策略的基础上变分推理和约束优化封装时空有效性。开发了新的深度神经网络架构，以实现具有动态潜在先验的生成和推理模型。所提出的方法代表了显着的定量和定性的改进，通过广泛的实验证明了现有的方法。该软件是公开的1。
A growing number of research areas such as location-based social networks, intelligent transportation systems, and urban computing utilize large amounts of trajectory data for benchmarking data management approaches and analysis methods. Given the general lackness of available large datasets, realistic synthetic trajectory datasets become important. This work proposes deep generative models for trajectory data that can learn disentangled models for sophisticated latent patterns. Existing methods rely on predefined heuristics and cannot learn the unknown underlying generative mechanisms. The proposed novel deep generative VAE-like models factorize global and local semantics (habits vs. random routing change). We further develop new inference strategies based on variational inference and constrained optimization to encapsulate spatiotemporal validity. New deep neural network architectures are developed to implement generative and inference models with dynamic latent priors. The proposed methods represent significant quantitative and qualitative improvements over existing approaches as demonstrated by extensive experiments. The software is made publicly available 1.