On-Demand Service Allocation via Historical Position Active Areas
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Solution Overview
Problem
Existing online to offline service systems, such as on-demand transportation services, face challenges in determining personalized active areas for service providers, leading to inefficient allocation of service requests and user experiences.
Innovation Solution
A system and method that utilize historical position data to identify candidate areas and active areas for service providers, prioritizing service requests based on position density and time-specific preferences, thereby optimizing the allocation of service requests to providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service requests are allocated to service providers without determining personalized active areas, then the allocation process is simple, but service efficiency and user experience deteriorate
Solution Approach 1:
The system performs preliminary actions by determining service providers' active areas in advance based on historical position data before allocating service requests. This allows the system to pre-categorize providers into different active area groups, so when a service request comes in, the system can quickly match requests with providers in the corresponding active area without complex real-time analysis, thus improving service efficiency while keeping the allocation process manageable.
Solution Approach 2:
The system segments service providers into different groups based on their personalized active areas (e.g., first active area, second active area, third active area). This segmentation allows the system to allocate service requests more efficiently by directing requests to providers in the most appropriate active area, improving overall service productivity without requiring the entire provider base to be considered for every request.
2Measurement precision
If historical position data is analyzed to identify active areas, then service request allocation accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of historical position data to identify and store service providers' active areas in advance. By pre-processing the historical data and establishing active area boundaries before actual service request allocation, the system achieves high measurement precision in identifying active areas without incurring processing delays during real-time request handling.
Solution Approach 2:
The system creates a simplified representation or copy of the complex historical position data by defining discrete active area boundaries (first active area, second active area, etc.). This copied representation can be quickly referenced during service request allocation without needing to re-analyze the original complex historical data, thus maintaining high identification accuracy while reducing processing time.
3Reliability
If service requests are allocated without considering provider preferences, then allocation speed is fast, but provider availability and service reliability decrease
Solution Approach 1:
The system applies local quality by matching service requests with providers based on local preferences and active areas. Instead of treating all providers uniformly, the system identifies which providers are actively available in specific geographic areas and prioritizes allocation to them. This localized matching approach improves service reliability by directing requests to providers who are both available and preferential for that area, without significantly reducing overall allocation speed.
Solution Approach 2:
The system performs preliminary classification of providers into different active area groups based on their preferences and historical behavior. This pre-categorization allows the allocation system to quickly match requests with appropriate providers by simply checking which pre-defined active area group contains available providers, maintaining fast allocation speed while improving reliability through preference-aware matching.
Data Source
AI summary
The present disclosure relates to systems and methods for providing online to offline services. The systems and methods may obtain a plurality of historical positions of a user terminal within a first predetermined time period in a plurality of days. The systems and methods may identify at least one candidate area based on the plurality of historical positions of the user terminal. The systems and methods may identify one or more of the at least one candidate areas as one or more active areas of the user terminal. The systems and methods may further provide data associated with one or more service requests relating to the one or more active areas to a customer application executed by the user terminal.