Crowd-Sourced Popularity Factors for Destination Estimation
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Solution Overview
Problem
Existing technologies fail to accurately estimate journey destinations for users traveling in unfamiliar areas or routes, as they rely on machine learning techniques that are ineffective in such scenarios.
Innovation Solution
A system that utilizes crowd-sourced popularity factors to estimate journey destinations by receiving location data, determining current routes, and suggesting destinations based on overall, variable, and saturation popularity scores, along with schedule data, to provide users with relevant suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to estimate journey destinations, then the system can provide personalized destination estimates for familiar routes, but it fails to accurately estimate destinations for users traveling in unfamiliar areas or routes
Solution Approach 1:
The patent introduces crowd-sourced popularity factors as an intermediary between the user's location data and the destination estimation. Instead of relying solely on the user's historical travel patterns (which fail in unfamiliar areas), the system uses aggregated popularity data from multiple users as a mediator to suggest destinations in unfamiliar locations. This intermediary data source bridges the gap when personal historical data is insufficient.
Solution Approach 2:
The system transitions from a personalized estimation approach (specific to individual users) to a universal approach that works for all users regardless of familiarity with the area. By incorporating crowd-sourced popularity factors that apply universally across multiple users, the system gains multi-functionality: it can estimate destinations both for familiar routes (using user history) and for unfamiliar areas (using crowd data).
Data Source
AI summary
The disclosure includes technology for estimating journey destinations based on crow-sourced popularity factors. The technology includes an example system including a processor and a memory storing instructions that when executed cause the system to: receive location data; determine a current route associated with a user based on the location data; determine one or more crowd-sourced popularity factors; estimate one or more destination estimations along the current route based on the one or more crowd-sourced popularity factors; and suggest the one or more destination estimations to the user.