Traffic trajectories of various vehicles, bicycles and pedestrians can help understand the traffic dynamics in a fine-grained manner like traffic flow, traffic congestion and ride-hailing demand. The comprehensive usage of traffic trajectory data has not been fully investigated due to the prevalent privacy concerns and commercial limitations. The traffic trajectory simulation task has emerged to generate high-fidelity trajectories in demand for downstream tasks to fill the gap between the scarce trajectory data and the widespread applications. Previous methods build the spatiotemporal dependencies of trajectories with Graph Neural Networks (GNNs) under generative adversarial training, yielding better yet unstable trajectory quality. We observe that the unsatisfied synthetic trajectories are caused by the insufficient spatiotemporal modeling of road networks and trajectory semantics. In this paper, we propose a novel patiomporal ted (STEGA) framework equipped with semantic-aware graph learning for traffic trajectory simulation to enable the explicit modeling of spatiotemporal dependencies throughout the learning pipeline. On the one hand, STEGA employs a graph encoder with the semantics of road networks for the spatial points of a trajectory, together with a time encoder for the time points. On the other hand, STEGA devises two spatiotemporal gates with the semantic graphs for the predictions of the future trajectory. Boosted by the semantic-aware graph learning, the proposed STEGA outperforms the counterparts consistently at both macro- and micro-level metrics on two datasets. Elaborate ablation studies and downstream tasks of the synthetic trajectories further demonstrate the superiority of STEGA. Our code is available at .

中文翻译：

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通过语义感知图学习进行时空门控交通轨迹模拟

各种车辆、自行车、行人的交通轨迹可以帮助细粒度了解交通流量、交通拥堵、网约车需求等交通动态。由于普遍存在的隐私问题和商业限制，交通轨迹数据的综合利用尚未得到充分研究。交通轨迹模拟任务的出现是为了生成下游任务所需的高保真轨迹，以填补稀缺轨迹数据与广泛应用之间的空白。先前的方法在生成对抗训练下使用图神经网络（GNN）构建轨迹的时空依赖性，产生更好但不稳定的轨迹质量。我们观察到，合成轨迹不满意是由于道路网络和轨迹语义的时空建模不足造成的。在本文中，我们提出了一种新颖的时空 ted (STEGA) 框架，配备用于交通轨迹模拟的语义感知图学习，以实现整个学习管道中时空依赖性的显式建模。一方面，STEGA 针对轨迹的空间点采用具有道路网络语义的图编码器，以及针对时间点的时间编码器。另一方面，STEGA 设计了两个带有语义图的时空门来预测未来的轨迹。在语义感知图学习的推动下，所提出的 STEGA 在两个数据集的宏观和微观层面指标上始终优于同行。详细的消融研究和合成轨迹的下游任务进一步证明了 STEGA 的优越性。我们的代码可在 获取。