Travel Optimization Using Machine Learning Models
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Solution Overview
Problem
The increasing abundance of data sources and information poses challenges in determining optimal data values and sources for use, particularly in domains requiring accurate and personalized results, as existing methods are time and resource intensive and often provide generalized guidance insufficient for individualized settings.
Innovation Solution
A system that uses a non-transitory computer-readable medium to determine a virtual network based on user location and preferences by acquiring a request for a service, filtering service providers, and applying a machine learning model informed by other users' travel patterns and geographical information to aggregate relevant service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive searching of existing literature and data sources is performed to identify relevant data sources, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data from multiple sources into structured formats before actual analysis is needed. Virtual networks of data are created in advance, allowing rapid retrieval and analysis without exhaustive searching at the time of use.
Solution Approach 2:
The system creates virtual copies and representations of complex data relationships through virtual networks. Instead of analyzing original complex data structures directly, the system works with simplified virtual representations that capture essential relationships, enabling faster analysis while maintaining accuracy.
2Adaptability or versatility
If models are customized to fit specific circumstances and individual considerations, then adaptability and measurement precision are improved, but device complexity and loss of time worsen
Solution Approach 1:
The system segments the complex model customization task into manageable components. Virtual networks are divided into modular data structures that can be independently configured and combined. This allows personalized models to be built by assembling pre-defined modules rather than creating custom models from scratch, reducing complexity while maintaining adaptability.
Solution Approach 2:
The system implements dynamic model configuration where virtual networks can be adjusted and reconfigured based on specific user needs and circumstances. The modular structure allows the system to adapt to different scenarios by dynamically selecting and combining appropriate data sources and analysis parameters without requiring complete model redesign.
3Productivity
If generalized models are used to provide guidance across multiple scenarios, then productivity is improved, but measurement precision and adaptability worsen
Solution Approach 1:
The virtual network system provides universal functionality by creating data structures that can serve multiple purposes and scenarios. The same virtual network framework can be applied across different analysis types and user needs, enabling rapid deployment while maintaining the ability to customize for specific scenarios when required.
Data Source
AI summary
Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.