Road Network Representation Learning with the Third Law of Geography
This work addresses the challenge of learning effective road segment representations for tasks like traffic prediction or navigation, offering an incremental improvement by integrating the Third Law of Geography into existing frameworks.
The paper tackles the problem of road network representation learning by addressing the overemphasis on distance effects in existing methods, proposing a novel graph contrastive learning framework that incorporates the Third Law of Geography to improve representation similarity based on geographic configurations, resulting in significant performance improvements across three downstream tasks on two real-world datasets.
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.