Vehicle Prioritization for Fleet Sharing
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Solution Overview
Problem
Current fleet manager computing systems lack effective vehicle prioritization systems for vehicle-sharing and ride-hailing services, failing to provide users with optimal vehicle recommendations based on contextual information.
Innovation Solution
A method and system that utilize a processor to obtain contextual information, including vehicle selection history, location, and application data, to generate and transmit vehicle recommendations to a portable device, and assign vehicles to users based on their preferences and past usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fleet manager computing systems use basic vehicle assignment without prioritization, then system simplicity is maintained, but user satisfaction and fleet management efficiency deteriorate due to lack of personalized recommendations
Solution Approach 1:
The system performs preliminary actions by obtaining contextual information (vehicle selection history, location information, application information) before generating vehicle recommendations. This allows the system to proactively prepare personalized recommendations rather than reacting to simple assignment requests, thereby improving user satisfaction while maintaining manageable complexity through structured data collection
Solution Approach 2:
The system implements feedback mechanisms by continuously updating vehicle selection history information based on user choices and generating refined recommendations. The system uses feedback from user selections, location changes, and application context to dynamically adjust vehicle recommendations, creating an adaptive system that improves with use without requiring complex manual intervention
2Productivity
If fleet manager computing systems implement comprehensive vehicle prioritization with contextual analysis, then vehicle assignment efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The system collects and stores contextual information (vehicle selection history, location data, application information) in advance before vehicle assignment is needed. This preliminary data gathering enables rapid generation of personalized recommendations when assignment requests occur, improving fleet management efficiency without adding significant processing time at the point of decision
Solution Approach 2:
The system generates a prioritized list of vehicle recommendations rather than evaluating all possible vehicles comprehensively. By focusing on generating a manageable set of top recommendations based on contextual factors rather than exhaustively analyzing every vehicle option, the system achieves high fleet management efficiency with acceptable processing time
3Measurement precision
If fleet manager computing systems generate personalized vehicle recommendations based on contextual information, then user preferences are better matched, but data processing complexity increases
Solution Approach 1:
The system segments contextual information into distinct categories: vehicle selection history information, location information, and application information. This segmentation allows the system to process and analyze different types of data separately using appropriate methods for each, improving vehicle matching accuracy while keeping data processing complexity manageable through structured organization
Solution Approach 2:
The system uses a unified vehicle recommendation generation process that handles multiple types of contextual information (history, location, application data) through a single integrated approach. This multi-functional system processes diverse data types using consistent algorithms, improving matching accuracy without requiring separate complex processing pathways for each data type
Data Source
AI summary
A method and system are disclosed and include obtaining, using a processor configured to execute instructions stored in a nontransitory computer-readable medium and in response to receiving a vehicle-sharing request, contextual information, which includes at least one of (i) vehicle selection history information associated with a vehicle-sharing account corresponding to the vehicle-sharing request, (ii) location information associated with the vehicle-sharing request, and (iii) application information associated with a portable device corresponding to the vehicle-sharing request. The method also includes generating, using the processor, vehicle recommendations based on the contextual information. The method also includes transmitting, using the processor, a first set of vehicle recommendations to the portable device. The method also includes assigning, using the processor and in response to receiving a signal indicating a selection of a vehicle from the first set of vehicle recommendations, the vehicle to a user associated with the vehicle-sharing request.