Embedding-Based Geocoding for Unknown Address Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Geocoding of unknown addresses, particularly in cold-start cases, is challenging due to lack of historical delivery data, diverse writing standards, and unstructured address formats, leading to inaccurate location estimation in last-mile delivery and navigation systems.
Innovation Solution
A machine learning model embeds addresses and geocodes in the same high-dimensional space, using Random Fourier Features and self-supervised contrastive learning to align address and geocode embeddings, enabling accurate determination of geocodes for unknown addresses through similarity matching and neighborhood-level boundary prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional geocoding methods are used for unknown addresses, then the system can operate without historical data, but the location estimation accuracy deteriorates due to lack of historical delivery data and diverse writing standards
Solution Approach 1:
The system performs preliminary actions by training the machine learning model in advance with available address-geocode pairs from diverse geographies. The model learns to embed addresses and geocodes in a shared vector space before actual geocoding tasks, enabling it to make accurate predictions even for unknown addresses without requiring historical delivery data at runtime.
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation between addresses and geocodes. The machine learning model transforms both addresses and geocodes into a common embedding space, where similarity in the embedding space corresponds to semantic similarity. This intermediary representation enables accurate geocoding of unknown addresses by finding the nearest neighbor geocode in the embedding space.
2Adaptability or versatility
If the system uses diverse writing standards and unstructured address formats, then it can handle various address types, but the geocoding accuracy deteriorates due to lack of standardized formats
Solution Approach 1:
The machine learning model is designed with universal functionality to handle diverse address formats and writing standards across multiple geographies. The embedding-based approach naturally accommodates unstructured addresses, typos, and format variations by learning semantic representations rather than relying on rigid format matching. This universal system maintains high geocoding accuracy across different address styles.
3Measurement precision
If the system implements neighborhood-level boundary prediction, then it can improve geocode precision for unknown addresses, but the computational complexity increases
Solution Approach 1:
The patent segments the geocoding problem into two distinct tasks: (1) embedding generation for addresses and geocodes, and (2) nearest neighbor search in the embedding space. By dividing the problem this way, the system can use efficient vector similarity search algorithms rather than complex boundary prediction models, reducing computational complexity while maintaining precision.
Data Source
AI summary
Techniques for improving last mile transportation and planning execution are described. In an example, a description of an address is received. Whether the address is not associated with a known geocode is determined. Using an ML model, an embedding vector of the address in an embedding space is generated, the ML model trained to embed addresses and geocodes in the embedding space. Based on the embedding vector, one or more known geocodes is determined. The address is then associated with the one or more known geocodes.