Chen, Y., Cong, G., He, T., Huang, W., Ong, Y.-S., & Zhou, H. (2024). Road Network Representation Learning with the Third Law of Geography. Advances in Neural Information Processing Systems 37, 11789–11813. https://doi.org/10.52202/079017-0376
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research was supported in part by Distributed Smart Value Chain programme which is funded by A*STAR
under the Singapore RIE2025 Manufacturing, Trade and Connectivity (MTC) Industry Alignment
Fund-Pre-Positioning (Award No: M23L4a0001), as well as cash and in-kind contribution from Singapore Telecommunications Limited (Singtel), through Singtel Cognitive and Artificial Intelligence
Lab for Enterprises (SCALE@NTU).” This research was also partially funded by the Knut and Alice Wallenberg Foundation (KAW 2019.0550).