Spatially Aware Graph Auto-Encoders for Multi-Scale Road Similarity
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Solution Overview
Problem
Existing methods for computing road network similarity do not sufficiently consider spatial features and are subjective in weight setting, lacking a comprehensive feature measurement method.
Innovation Solution
A graph convolution auto-encoder network is employed to extract road network features, trained in an unsupervised manner, and uses cosine similarity to compute similarity between road networks, considering global, local, and connection features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use artificially defined rules for similarity computation, then the computation process is simple, but the measurement precision is insufficient and spatial features are not considered sufficiently
Solution Approach 1:
The patent replaces traditional mechanical rule-based similarity computation with a deep learning-based graph convolution auto-encoder system. The model automatically learns spatial features and similarity patterns from road network data, substituting manual feature engineering and artificial rules with automated neural network-based feature extraction and computation, thereby improving measurement precision while managing complexity through end-to-end training.
2Measurement precision
If multi-factor integration method is used, then the measurement precision is improved, but the weight setting between factors becomes too subjective
Solution Approach 1:
The graph convolution auto-encoder model performs self-service by automatically learning optimal feature weights and representations during unsupervised training. The model minimizes reconstruction error of the input graph structure, which inherently optimizes the importance (weight) of different spatial features without requiring manual weight setting. This eliminates subjectivity in weight assignment while maintaining high measurement precision through data-driven feature importance learning.
3Measurement precision
If graph convolution auto-encoder model is used for deep feature extraction, then the measurement precision and human cognition alignment are improved, but the device complexity and training requirements increase
Solution Approach 1:
The patent segments the similarity computation task into distinct modular components: graph construction from road network data, feature extraction through graph convolution layers, dimensionality reduction via auto-encoder bottleneck, and final similarity computation. This segmentation allows each component to be optimized independently and facilitates training through backpropagation across the entire pipeline, managing complexity while achieving high precision through specialized processing at each stage.
Data Source
AI summary
Disclosed is a graph convolution auto-encoder based method for computing road network similarity. The method includes: creating a dual graph of a road network, and giving road network space feature information to nodes of the dual graph from three aspects of global, local and connection characteristics on the basis of a relation principle between an entire structure and parts of the structure, such that a quantitative expression of a road network graph structure is obtained; aggregating and updating node feature information and structure information of a road network graph by the graph convolution auto-encoder, and forming a deep understanding of the road network, such that a coded expression of node information of the road network is obtained; and mapping a complex high-dimensional feature space to an easy-to-measure low-dimensional feature space through an average pooling operation, so as to obtain a set of feature vectors, and computing the similarity.