# Road Network Representation Learning with the Third Law of Geography Source: [Road Network Representation Learning with the Third Law of Geography]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:53:24.210Z Authors: Haicang Zhou, Weiming Huang, Yile Chen, Tiantian He, Gao Cong, Yew-Soon Ong Published: 2024-06-06T13:04:43.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Road Function Prediction on Singapore](): Road Function Prediction; Singapore; Micro-F1: 81.4. - [Road Traffic Inference on Singapore](): Road Traffic Inference; Singapore; MAE: 2.8. - [Visual Road Retrieval on Singapore](): Visual Road Retrieval; Singapore; Recall@10: 0.46. - [Road Function Prediction on NYC](): Road Function Prediction; NYC; Micro-F1: 82.97. - [Road Traffic Inference on NYC](): Road Traffic Inference; NYC; MAE: 3.3. - [Visual Road Retrieval on NYC](): Visual Road Retrieval; NYC; Recall@10: 0.5531. ## Original sources and implementation [Original paper]() [PDF]()