Geolocation Trajectory Guest Rider Detection
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Solution Overview
Problem
Current ride-sharing systems lack an efficient method to automatically determine whether a rider is an account holder or a guest rider, which can lead to missed opportunities for fraud detection and inappropriate driver assignments.
Innovation Solution
A geolocation-based system that compares the location data of an account holder's wireless device with the pickup and drop-off locations and the driver's location to determine with certainty whether the rider is an account holder or a guest rider, using machine learning models to predict threshold distances and classify guest riders into types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification methods are used to determine rider identity, then system complexity is reduced, but detection precision and automation level deteriorate
Solution Approach 1:
The system segments the guest rider detection process into distinct functional modules: a geolocation system to obtain location data, a machine learning model to predict threshold distances, and a classification system to determine rider type. This segmentation allows each module to specialize in a specific task, improving overall detection accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw geolocation data and rider identity determination. The ML models act as mediators that process location data, predict threshold distances, and provide probabilistic assessments, thereby enhancing detection precision without requiring direct complex rule-based logic in the core system.
2Extent of automation
If automated geolocation-based detection is implemented, then detection precision and automation level improve, but system complexity and computational requirements worsen
Solution Approach 1:
The system implements self-service automation where the machine learning models automatically process geolocation data and determine rider identity without manual intervention. The models self-adjust by learning from historical data, automatically predicting threshold distances and classifying rider types, thereby achieving high automation while the models handle the computational complexity internally.
Solution Approach 2:
The patent utilizes parameter changes in the form of dynamic threshold distances predicted by machine learning models. Instead of using fixed geometric rules, the system dynamically adjusts the threshold parameter based on learned patterns from historical data, enabling automated detection that adapts to varying conditions while managing complexity through parameter optimization.
3Adaptability or versatility
If geometric rules with fixed thresholds are used, then system complexity is reduced, but adaptability and detection accuracy worsen
Solution Approach 1:
The system transitions from static geometric rules to dynamic machine learning-based threshold prediction. The threshold distance becomes a dynamic parameter that adapts based on the model's predictions from historical data, allowing the system to flexibly adjust to different ride scenarios, locations, and patterns while the ML model manages the complexity of adapting to varying conditions.
Data Source
AI summary
A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.