Refuse Container Identification Using GPS-Parcel Correlation
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Solution Overview
Problem
In dense urban areas, it is challenging for refuse collection companies to accurately determine which refuse containers are associated with specific customers, as container placements can be inconsistent and often located near each other, making traditional methods ineffective.
Innovation Solution
A method and system that utilize sensor data from refuse collection vehicles (RCVs) to detect operational states and correlate location information with parcel data, allowing for the identification of the entity associated with a container by analyzing triggering conditions such as lift arm positions, hopper openings, and GPS coordinates, and adjusting these coordinates based on RCV configuration to match land parcel boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify refuse containers, then the process is simple, but the accuracy of determining which container is associated with a specific customer deteriorates in dense urban areas
Solution Approach 1:
The patent uses GPS location data as an intermediary to bridge the container and customer association. Instead of directly identifying containers through complex visual or manual methods, the system captures GPS coordinates when the RCV services a container and uses these coordinates to match with customer locations in a database, automatically determining ownership without direct observation or manual tracking
Solution Approach 2:
The patent replaces traditional mechanical identification methods (visual inspection, manual recording, physical tags) with an electronic GPS-based system. The location sensor electronically captures coordinates and the processor automatically matches them with database records, substituting mechanical human effort with electronic automation to improve accuracy while managing complexity
2Productivity
If manual tracking methods are used, then the system is easy to operate, but productivity and billing accuracy deteriorate
Solution Approach 1:
The system performs self-service by automatically capturing GPS data, processing location information, matching containers with customers, and generating billing records without human intervention. The RCV's onboard systems autonomously complete the identification and billing processes, eliminating manual tracking operations and significantly improving productivity
Solution Approach 2:
The system implements feedback loops where GPS data from the RCV is continuously captured, processed, and used to update container-customer associations. The processed information feeds back into the database to verify service delivery and generate billing records, creating a closed-loop system that automatically adjusts and confirms operational accuracy
Data Source
AI summary
Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.