In this study, we introduce a novel framework called Toast for learning
general-purpose representations of road networks, along with its advanced
counterpart DyToast, designed to enhance the integration of temporal dynamics
to boost the performance of various time-sensitive downstream tasks.
Specifically, we propose to encode two pivotal semantic characteristics
intrinsic to road networks: traffic patterns and traveling semantics. To
achieve this, we refine the skip-gram module by incorporating auxiliary
objectives aimed at predicting the traffic context associated with a target
road segment. Moreover, we leverage trajectory data and design pre-training
strategies based on Transformer to distill traveling semantics on road
networks. DyToast further augments this framework by employing unified
trigonometric functions characterized by their beneficial properties, enabling
the capture of temporal evolution and dynamic nature of road networks more
effectively. With these proposed techniques, we can obtain representations that
encode multi-faceted aspects of knowledge within road networks, applicable
across both road segment-based applications and trajectory-based applications.
Extensive experiments on two real-world datasets across three tasks demonstrate
that our proposed framework consistently outperforms the state-of-the-art
baselines by a significant margin.

Toast 和 DyToast 是用于学习道路网络通用表示的新框架，增强了时间动态集成以提高各种时间敏感下游任务的性能。通过编码交通模式和旅行语义来实现对道路网络的两个关键语义特征的重要性概括，Toast 通过引入辅助任务和 Transformer 预训练策略，而 DyToast 通过使用统一三角函数更有效地捕获道路网络的时间演变和动态特性。我们的实验证明，这些提出的技术可以在基于路段和基于轨迹的应用中获得编码了多方面知识的表示，并且一致优于现有技术的基准。