  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.
