Predictive Location Selection for On-Demand Services
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Solution Overview
Problem
Existing network computer systems for on-demand services face inefficiencies due to manual user selection of service locations, which can lead to delays, cancellations, and poor service quality, especially in high-traffic areas with complex road layouts.
Innovation Solution
The network computer system implements predictive location selection by determining a service area with the highest probability of matching with a service provider based on factors such as travel speed, distance, directional heading, and historical availability data, before the user submits a service request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select service locations, then users have control over their pickup points, but this leads to delays, cancellations, and poor service quality
Solution Approach 1:
The system pre-determines optimal service locations before the user submits a service request, using real-time data about service provider positions, travel speeds, and historical availability. This preliminary action eliminates the need for manual user selection during the request process, reducing delays and cancellations while maintaining high service efficiency.
2Productivity
If the system preselects service locations, then service efficiency improves and wait times reduce, but the system complexity increases
Solution Approach 1:
The system uses automated algorithms to self-determine optimal service locations based on real-time data from service providers and historical patterns. This self-service capability eliminates the need for complex manual intervention or user input, achieving high service efficiency through automated decision-making that manages system complexity internally.
3Measurement precision
If the system considers multiple factors for location selection, then matching accuracy with service providers improves, but the computational requirements increase
Solution Approach 1:
The system focuses computational resources on analyzing factors specifically relevant to each user's local context, such as nearby service providers, local traffic conditions, and area-specific historical data. This localized approach maintains high matching accuracy by considering only the most relevant factors for each specific location rather than processing all possible factors globally, thereby reducing unnecessary computational energy consumption.
Data Source
AI summary
A system can receive location data from a computing device of a requesting user, where the location data indicates a current position of the requesting user. The system can determine a rendezvous location for the requesting user prior to the requesting user transmitting a service request to the network computer system. The system may then transmit data corresponding to the rendezvous location to the computing device of the requesting user. The system may further periodically receive an update request from the computing device of the user, and for each update request, (i) determine a second plurality of transport providers with the predetermined distance or time from the current position of the user, and (ii) based on respective locations of these transport providers, transmit updated map data to the computing device to indicate an updated rendezvous location on the map interface.