Inferred Pickup Location Clustering for Complex Building Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional navigation systems, such as those using GPS data, struggle to provide accurate location data for specific apartment numbers, building names, and room numbers within larger buildings, making transportation service navigation challenging.
Innovation Solution
A network system that utilizes historical location data from service provider and requester devices to generate clustered location data, identifying the centroid of the largest cluster within a threshold distance of a target address to determine an inferred accurate location, which is then used for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS satellite data is used to determine location, then global coverage and basic positioning are achieved, but accuracy for specific locations within large buildings (apartment numbers, room numbers) is insufficient
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical location data from multiple devices before actual navigation is needed. This historical data is pre-processed to identify clusters of locations, so when a navigation request is made, the system can quickly retrieve and use the pre-analyzed cluster information rather than performing complex analysis in real-time.
Solution Approach 2:
The patent introduces an intermediary element - the clustered historical location data - that mediates between the GPS satellite data and the final navigation instructions. Instead of directly using raw GPS coordinates, the system uses these intermediary cluster centroids as reference points to determine accurate locations within complex buildings, bridging the gap between satellite-level precision and building-level specificity.
2Loss of information
If traditional point of interest data is used, then basic location information is provided, but detailed navigation to specific destinations within buildings is not reliable
Solution Approach 1:
The system merges multiple sources of location information - GPS satellite data, traditional point of interest data, and newly collected historical device location data - to create a more complete and accurate location picture. By combining these diverse data sources and analyzing them together through clustering, the system recovers lost location details and achieves precise destination identification within buildings.
Solution Approach 2:
The patent adds another dimension to location data by incorporating historical temporal information and spatial clustering patterns. Instead of relying solely on static point of interest data or single-point GPS coordinates, the system analyzes location data across multiple dimensions - historical time sequences, spatial distributions, and cluster relationships - to infer accurate destinations within complex buildings.
3Ease of operation
If GPS data alone is used for navigation instructions, then simple routing is provided, but accurate pickup, drop-off, and parking location identification within complex environments is not achieved
Solution Approach 1:
The system segments the navigation process into distinct phases: (1) route planning using traditional GPS data for overall path determination, (2) destination identification using clustered historical location data to pinpoint exact pickup, drop-off, and parking locations, and (3) final navigation guidance. This segmentation allows the system to maintain simplicity in route overview while achieving high precision in critical location identification.
Solution Approach 2:
The system performs preliminary analysis of historical location data to identify and store clustered location patterns before actual navigation is needed. This pre-computed cluster information is readily available when navigation requests are made, enabling quick and accurate identification of pickup, drop-off, and parking locations without adding complexity to the real-time navigation operation.
Data Source
AI summary
Systems and methods herein describe a network system for generating inferred accurate locations. The systems and methods receive a transportation trip request from a first computing device that includes a target address, access a first plurality of historical location data and a second plurality of historical data, generate clustered location data using the first plurality of historical location data and the second plurality of historical location data, select a subset of cluster locations from the clustered location data, determine an inferred accurate location address, and modify the transportation trip request by associating the inferred accurate location address with the target address.