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Road Network Representation Learning with the Third Law of Geography

Haicang Zhou, Weiming Huang, Yile Chen, Tiantian He, Gao Cong, Yew-Soon Ong

TL;DR

Garner addresses limitations of distance-driven road-network representations by integrating the Third Law of Geography through geographic-configuration-aware augmentation and spectral negative sampling within a graph-contrastive framework. It fuses Third Law cues with the traditional First Law via a dual-contrastive objective across three graph views, including street-view-derived configurations and topology-based diffusion. Empirical results on two real-city datasets show Garner delivering state-of-the-art performance for road function prediction, traffic inference, and visual road retrieval, validating the practical impact of incorporating geographic configurations. This work advances unsupervised road-network representation learning by leveraging multimodal geographic context to produce more accurate and generalizable road embeddings.

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.

Road Network Representation Learning with the Third Law of Geography

TL;DR

Garner addresses limitations of distance-driven road-network representations by integrating the Third Law of Geography through geographic-configuration-aware augmentation and spectral negative sampling within a graph-contrastive framework. It fuses Third Law cues with the traditional First Law via a dual-contrastive objective across three graph views, including street-view-derived configurations and topology-based diffusion. Empirical results on two real-city datasets show Garner delivering state-of-the-art performance for road function prediction, traffic inference, and visual road retrieval, validating the practical impact of incorporating geographic configurations. This work advances unsupervised road-network representation learning by leveraging multimodal geographic context to produce more accurate and generalizable road embeddings.

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.
Paper Structure (37 sections, 19 equations, 3 figures, 7 tables)

This paper contains 37 sections, 19 equations, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Preliminaries and problem definition
  4. Method
  5. Representation of geographic configuration and data preprocessing
  6. Geographic configuration aware graph augmentation
  7. Graph encoder
  8. Contrastive loss
  9. Spectral negative sampling
  10. Fusing the Third Law and the First Law
  11. Experiments
  12. Experimental setups
  13. Datasets
  14. Downstream tasks
  15. Evaluation metrics
  16. ...and 22 more sections

Figures (3)

  • Figure 1: Architecture of Geographic law aware road network representation learning (Garner).
  • Figure 2: Sensitivity analysis of hyper-parameter $k$ and $d$.
  • Figure 3: Sensitivity analysis of hyper-parameter $k$ and $d$.

Theorems & Definitions (5)

  • Definition 1: Graph
  • Definition 2: Graph Laplacian matrix
  • Definition 3: Road Network
  • Definition 4: The First Law of Geography, or Tobler's First Law of Geography, miller2004tobler
  • Definition 5: The Third Law of Geography, zhu2022third