Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, and travel time estimation). However, most existing methods only contrast within the same scale, i.e., treating road network and trajectory separately, which ignores valuable inter-relations. In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. We design domain-specific augmentations for road-road contrast and trajectory-trajectory contrast separately, i.e., road segment with its contextual neighbors and trajectory with its detour replaced and dropped alternatives, respectively. On top of that, we further introduce the road-trajectory cross-scale contrast to bridge the two scales by maximizing the total mutual information. Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies. We conduct prudent experiments based on two real-world datasets with four downstream tasks, demonstrating improved performance and effectiveness. The code is available at https://github.com/mzy94/JCLRNT.

翻译：道路网络与轨迹的表示学习对于交通系统至关重要，因为学习到的表示可直接用于多种下游任务（如交通速度推断和出行时间估计）。然而，现有方法大多仅在单一尺度内进行对比，即分别处理道路网络和轨迹，忽略了两者间有价值的关联关系。本文旨在提出一个统一框架，实现道路网络与轨迹表示的端到端联合学习。我们分别为道路-道路对比和轨迹-轨迹对比设计了特定领域的增强策略：道路片段与其上下文邻域进行对比，轨迹则通过绕行替换和丢弃替代方案进行对比。在此基础上，我们进一步引入道路-轨迹跨尺度对比，通过最大化总互信息来桥接两个尺度。与现有仅对比图与其所属节点的跨尺度对比学习方法不同，我们的道路片段与轨迹对比通过新颖的正采样和自适应加权策略进行了精细定制。基于两个真实世界数据集和四个下游任务的审慎实验表明，该方法在性能与有效性上均有提升。代码已开源至 https://github.com/mzy94/JCLRNT。