我们提出一种人类移动的生成模型，其中轨迹作为在空间交互网络上定义的、随时间变化的马尔可夫动态的实现而产生。该模型构建了包含枢纽、通道、支线路径和地铁连接的分层路由结构，并利用类似引力的距离衰减函数结合外部施加的时间调度与方向性偏差来指定转移矩阵。人口质量以不可区分且无记忆的移动者形式演化，每个时间步仅执行一次转移。在聚合后，生成的轨迹再现了反映网络几何结构、时间调制及连通性约束的有序起止流。通过将Perron-Frobenius定理应用于每日演化算子，我们识别出一个唯一的周期性不变人口分布，作为自然的非瞬态参考状态。我们验证了轨迹层面实现与多步马尔可夫动态之间的一致性，表明差异完全归因于有限种群采样。该框架提供了一种以网络为中心、保护隐私的移动轨迹生成方法，并可在不引入个体行为假设的前提下研究时间流逝下的流动结构。

We present a generative model of human mobility in which trajectories arise as realizations of a prescribed, time-dependent Markov dynamics defined on a spatial interaction network. The model constructs a hierarchical routing structure with hubs, corridors, feeder paths, and metro links, and specifies transition matrices using gravity-type distance decay combined with externally imposed temporal schedules and directional biases. Population mass evolves as indistinguishable, memoryless movers performing a single transition per time step. When aggregated, the resulting trajectories reproduce structured origin-destination flows that reflect network geometry, temporal modulation, and connectivity constraints. By applying the Perron-Frobenius theorem to the daily evolution operator, we identify a unique periodic invariant population distribution that serves as a natural non-transient reference state. We verify consistency between trajectory-level realizations and multi-step Markov dynamics, showing that discrepancies are entirely attributable to finite-population sampling. The framework provides a network-centric, privacy-preserving approach to generating mobility trajectories and studying time-elapsed flow structure without invoking individual-level behavioral assumptions.