Destination-Specific Arrival Prediction for Consistent Alerts
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Solution Overview
Problem
Current arrival prediction technologies are inaccurate due to reliance on geo-fences and inconsistent location reporting from mobile devices, failing to account for location-specific factors and assuming direct travel to destinations, leading to unreliable arrival alerts in services like ridesharing and delivery applications.
Innovation Solution
A destination-specific model is generated through machine learning on past trip data to predict arrival times, considering factors such as traffic, likely stops, and routes, adjusting predictions based on user progress and destination readiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If geo-fence is used for arrival alerts, then the system is simple to implement, but the accuracy of arrival predictions deteriorates
Solution Approach 1:
The patent segments the arrival prediction problem into multiple components: destination-specific modeling, traffic condition analysis, route optimization, and real-time location tracking. By dividing the system into these functional modules, it achieves high accuracy without excessive implementation complexity, as each segment can be developed and optimized independently.
Solution Approach 2:
The patent introduces destination-specific models as intermediary components between the geo-fence system and the arrival alert mechanism. These models act as mediators that process location data through multiple factors (traffic, route, destination characteristics) to generate accurate predictions, bridging the gap between simple geo-fencing and complex prediction requirements.
2Measurement precision
If location reporting frequency is increased, then arrival prediction accuracy improves, but energy consumption increases
Solution Approach 1:
The patent implements dynamic location reporting where the reporting frequency and precision are adjusted based on the user's proximity to the destination and current movement patterns. As users approach the destination, the system increases monitoring intensity, while maintaining lower energy consumption during transit phases, optimizing the balance between accuracy and energy usage.
Solution Approach 2:
The system dynamically changes reporting parameters (frequency, precision, update intervals) based on contextual factors such as distance to destination, traffic conditions, and predicted arrival time. This parameter adaptation allows the system to maintain accuracy when needed while minimizing energy consumption during normal travel conditions.
3Measurement precision
If destination-specific modeling is implemented, then arrival prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates universal destination-specific models that can be applied across multiple destinations and service types (ridesharing, delivery, logistics). By designing a multi-functional modeling framework that handles various destination characteristics and service requirements, the system achieves high accuracy without proportionally increasing complexity, as the same core model serves multiple purposes.
Solution Approach 2:
The system performs preliminary destination-specific modeling and route analysis in advance, before the user actually travels. By pre-processing destination characteristics, traffic patterns, and optimal routes, the system reduces real-time computational complexity while maintaining high prediction accuracy during actual travel and arrival monitoring.
Data Source
AI summary
The disclosed technology provides accurate arrival alerts to a destination. In some embodiments the disclosed technology utilizes a destination specific model that can take into account location specific factors such as traffic around the destination, best approaches to the destination, other likely intermediate stops on the way to the destination, etc. In some embodiments, the present technology aims to provide an arrival alert to the destination at a consistent time interval prior to a user arriving at the destination.