Geocoding Confidence Scoring and Manual Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current routing systems face challenges in accurately determining locations from ambiguous address inputs and struggle to efficiently optimize delivery plans due to dynamic order changes, leading to inefficiencies and delayed dispatches.
Innovation Solution
A computerized method for geocoding corrections in a routing system that includes user-side mobile devices with a routing application, enabling end-users to input addresses, generate confidence scores, and display draggable markers on a web map, allowing for manual correction of geocoded locations, which are then posted to a routing service, thereby improving geocoding accuracy and optimizing delivery plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated geocoding is used to convert addresses to latitude/longitude, then processing speed is improved, but location accuracy deteriorates for ambiguous addresses
Solution Approach 1:
The system displays geocoded locations on a map and allows users to provide feedback by dragging markers to correct locations. This feedback mechanism enables the system to learn from user corrections and improve future geocoding accuracy while maintaining fast automated processing.
Solution Approach 2:
A confidence score intermediary is introduced between automated geocoding and final location selection. Low confidence scores trigger manual verification, allowing the system to maintain automated processing for high-confidence cases while ensuring accuracy for ambiguous addresses through selective human intervention.
2Reliability
If the routing engine processes all orders to optimize delivery plans, then route optimization is improved, but response time deteriorates due to computational complexity
Solution Approach 1:
The system pre-calculates and stores geocoded locations and confidence scores before routing optimization. This preliminary processing separates geocoding from route optimization, allowing the routing engine to work with pre-processed data and reduce computational time while maintaining optimization quality.
Solution Approach 2:
The system segments the routing process into independent stages: geocoding with confidence scoring, manual verification for low-confidence cases, and route optimization. This segmentation allows parallel processing and prevents bottlenecks, improving both optimization quality and response time.
3Measurement precision
If manual verification of geocoded locations is performed, then location accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system changes the parameter of verification intensity based on confidence scores. High-confidence geocodes are accepted automatically without manual verification, while low-confidence geocodes trigger manual verification. This parameter-based filtering maintains accuracy for critical cases while preserving processing speed for routine cases.
Data Source
AI summary
In one aspect, a computerized method for automatically implementing geocoding corrections in a routing system includes the step of providing a user-side mobile device, wherein the user-side mobile device comprises a routing application and display a web map. The method includes the step of geocoding a correction to the latitude and longitude of a point of interest. The method includes the step of enabling an end-user of the user-side mobile device to enters an address string into the routing application. The method includes the step of geocoding the address string to a best possible location. The method includes the step of generating a confidence score for the geocoded location. The method includes the step of providing the display of the web map with a set of draggable location markers on the web map with a confidence-sorted list on the side of the display. The method includes the step of posting a correctly geocoded location with a routing service using the routing application.