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.

翻译：本研究提出了一种新颖的框架Toast，用于学习道路网络的通用表征，并进一步提出了其增强版本DyToast，旨在通过整合时序动态特性提升各类时间敏感下游任务的性能。具体而言，我们提出编码道路网络固有的两种核心语义特征：交通模式与出行语义。为实现这一目标，我们改进了skip-gram模块，通过引入辅助目标来预测目标路段相关的交通上下文。此外，我们利用轨迹数据并基于Transformer设计预训练策略，以提取道路网络中的出行语义。DyToast通过采用具有良好性质的统一三角函数进一步增强了该框架，从而更有效地捕捉道路网络的时序演化与动态特性。通过所提出的技术，我们能够获得编码道路网络多层面知识的表征，这些表征可适用于基于路段和基于轨迹的应用。在三个任务上的两个真实数据集上进行的广泛实验表明，所提出的框架始终以显著优势超越最先进的基线方法。