Road network representation learning aims to learn compressed and effective
vectorized representations for road segments that are applicable to numerous
tasks. In this paper, we identify the limitations of existing methods,
particularly their overemphasis on the distance effect as outlined in the First
Law of Geography. In response, we propose to endow road network representation
with the principles of the recent Third Law of Geography. To this end, we
propose a novel graph contrastive learning framework that employs geographic
configuration-aware graph augmentation and spectral negative sampling, ensuring
that road segments with similar geographic configurations yield similar
representations, and vice versa, aligning with the principles stated in the
Third Law. The framework further fuses the Third Law with the First Law through
a dual contrastive learning objective to effectively balance the implications
of both laws. We evaluate our framework on two real-world datasets across three
downstream tasks. The results show that the integration of the Third Law
significantly improves the performance of road segment representations in
downstream tasks.

通过使用地理配置感知的图增强和谱负采样的新型图对比学习框架，本文提出了一种将道路网络表示赋予地理第三定律的方法，以实现道路段具有类似地理配置的段生成类似表示，以及相反情况下的对齐。该框架通过双对比学习目标有效平衡了第一定律和第三定律的影响，实现了第一定律和第三定律的结合。在两个真实世界数据集上进行了三个下游任务的评估，结果显示第三定律的融合显著改善了道路段表示在下游任务中的性能。