Although human trajectory data that are collected passively from location-based services (LBS) are regarded as a substitute for household travel surveys that entail a larger cost, the reality is that the data cannot be utilized directly for transportation planning and policy making without imputing missing qualitative information. Deep learning technologies have been widely used to infer the hidden features of passively collected mobile data. A deep neural network, however, is so deterministic that the probabilistic aspect of activity inference cannot be accommodated. In the present study, a stochastic approach (VAE-HMM) was devised to generate human activity chains by incorporating a variational autoencoder (VAE) with a hidden Markov model (HMM). Whereas an original HMM clusters data in the observational space, the proposed approach conducts clustering in a latent space with a smaller dimension. The VAE contributes by both reducing the input dimensionality and by sidestepping the overfit to sample data. The variational inference (VI) method was used to estimate the parameters of VAE-HMM within a Bayesian framework. Data drawn from spatio-temporal, demographic, socio-economic, and individual-specific sources were chosen as input variables to feed the model. The VAE-HMM can be trained in either a supervised or an unsupervised manner.

虽然从基于位置的服务 (LBS) 被动收集的人类轨迹数据被认为可以替代成本更高的家庭旅行调查，但现实情况是，这些数据不能直接用于交通规划和政策制定，而不会对缺失进行估算。定性信息。深度学习技术已被广泛用于推断被动收集的移动数据的隐藏特征。然而，深度神经网络的确定性如此之高，以至于无法适应活动推理的概率方面。在本研究中，设计了一种随机方法 (VAE-HMM)，通过将变分自编码器 (VAE) 与隐马尔可夫模型 (HMM) 相结合来生成人类活动链。而原始 HMM 在观测空间中聚类数据，所提出的方法在具有较小维度的潜在空间中进行聚类。VAE 通过降低输入维度和避免对样本数据的过度拟合做出贡献。变分推理（VI）方法用于在贝叶斯框架内估计 VAE-HMM 的参数。从时空、人口、社会经济和个人特定来源中提取的数据被选为输入变量以提供模型。VAE-HMM 可以以有监督或无监督的方式进行训练。选择社会经济和个人特定的来源作为输入变量来提供模型。VAE-HMM 可以以有监督或无监督的方式进行训练。选择社会经济和个人特定的来源作为输入变量来提供模型。VAE-HMM 可以以有监督或无监督的方式进行训练。