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:道路网络和轨迹代表学习对于交通系统至关重要,因为学习到的代表性可以直接用于各种下游任务(例如,交通速度推断和旅行时间估计),但是,大多数现有方法只在同一个尺度上形成对比,即分别处理道路网络和轨迹,忽视了宝贵的相互关系。在本文件中,我们的目标是提出一个统一框架,共同学习道路网络和轨迹代表端至端对端。我们设计一个统一的框架,用于路路路路对比和轨轨迹对比的域别扩增,即路段与周围环境相邻的路段和轨迹,并分别更换和丢弃其绕行方式。此外,我们进一步引入道路轨迹跨尺度对比,通过尽量扩大全部相互信息,弥合两个尺度。与现有图表上的跨尺度对比学习方法不同,路段和轨迹之间的对比通过新颖的积极抽样和适应加权战略精心调整。我们根据两个真实世界数据集进行谨慎的实验,有4个下游任务:MLM/RM。我们可在https/NTGscomm。