Dynamic Vehicle Rental Rate and Location Management
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Solution Overview
Problem
Current vehicle rental management systems lack flexibility in pick-up and return locations and pricing, failing to account for various data influencing vehicle movement such as driving conditions and driver behavior, leading to suboptimal automation and increased labor costs for rental companies.
Innovation Solution
A method and device that collect and transmit data on vehicle and user parameters, including driving conditions and driver behavior, to a management center, enabling real-time updates to rental rates and offering flexible pick-up and return locations based on collected data, allowing for customizable pricing and optimized fleet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed pick-up and return locations are used, then operational simplicity is maintained, but user flexibility and service optimization are reduced
Solution Approach 1:
The system dynamically determines pick-up and return locations based on real-time data analysis rather than using fixed predetermined locations. The management center processes multiple data sources (user profile, vehicle characteristics, weather, traffic, geographic information) to dynamically optimize locations, allowing the system to adapt to changing conditions while maintaining operational efficiency through automated decision-making.
Solution Approach 2:
A management center acts as an intermediary between users and the vehicle rental system. This intermediary processes complex data analysis and location optimization algorithms, handling the computational complexity centrally rather than requiring complex distributed systems at each rental point. The management center mediates between multiple data sources and the final location decision, simplifying the overall system architecture.
2Adaptability or versatility
If static pricing frameworks are used, then pricing simplicity is maintained, but responsiveness to real-time conditions and user behavior is reduced
Solution Approach 1:
The system implements continuous feedback loops where sensor data from vehicles, user behavior data, weather conditions, and traffic information are constantly collected and processed. This feedback is used to dynamically adjust pricing in real-time, allowing the system to respond to changing conditions. The management center analyzes this feedback data and updates pricing frameworks accordingly, creating a responsive pricing mechanism that adapts to actual usage patterns and external conditions.
Solution Approach 2:
The pricing framework transitions from static parameters to dynamic parameters that change based on real-time data. Multiple variables (vehicle usage patterns, driver behavior, weather conditions, traffic状况, location demand) are used as changing parameters that directly influence pricing. This allows the system to optimize pricing based on actual conditions rather than predetermined rates, improving revenue management and resource allocation.
3Measurement precision
If comprehensive sensor data collection is implemented, then vehicle monitoring precision is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs universal sensor platforms and data collection mechanisms that serve multiple functions simultaneously. Sensors collect data for various purposes (vehicle condition monitoring, usage pattern analysis, maintenance scheduling, pricing optimization) through a unified data collection framework. This multi-functional approach reduces overall system complexity by consolidating data collection and processing infrastructure rather than requiring separate systems for each function.
Solution Approach 2:
The management center serves as an intermediary that consolidates and processes data from multiple sensor sources. Rather than requiring complex distributed processing at each sensor node, the system centralizes data management and analysis functions. This intermediary approach simplifies the overall architecture by handling data aggregation, validation, and analysis in a centralized location, reducing the computational complexity required at individual sensor points.
Data Source
AI summary
The method for managing the rental of vehicles (1), comprising at least one step of collecting at least one point of data relating to the vehicle (1), one step of transferring said data to a rental vehicle management center (4), and one step of sending at least one instruction from the management center (4) to the vehicle, characterized in that it also comprises at least one step of collecting and sending at least one point of data relating to the user of the vehicle (1) to the management center (4) and one step of sending the user a final updated rate according to the data received by the management center (4). The invention also relates to a device for implementing the invention.