Adaptive Transit Prediction Model Selection
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Solution Overview
Problem
Current logistics modeling and prediction methods require large amounts of training data and complex models to accurately predict transit times, which is inefficient and computationally resource-intensive, especially due to the irregular and context-dependent nature of shipping fluctuations throughout the year.
Innovation Solution
A method that iteratively selects and reselects a model from a set of trained models based on specific evaluation and prediction periods, accounting for seasonal, current, and predicted shipping behavior, weather, and other factors, using a diverse range of models and data sets to adapt quickly to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of training data and complex models are used, then prediction accuracy is improved, but computational resource consumption and model complexity increase
Solution Approach 1:
The patent segments the logistics prediction problem into multiple specialized models, each trained on specific data subsets or for specific time periods. Instead of using one complex model for all scenarios, the system divides the modeling task into smaller, more manageable segments that can be selected and applied based on the specific prediction context, thereby reducing the complexity of individual models while maintaining overall prediction accuracy.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting model selection and training parameters based on the specific prediction context. Different models are chosen or retrained with different parameters depending on the time period, logistics route, or data availability, allowing the system to achieve high accuracy without consistently using the most complex model configuration for every prediction scenario.
2Measurement precision
If large amounts of training data are used, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple models on different data subsets before the actual prediction is needed. These models are prepared in advance and can be quickly selected and applied when predictions are required, eliminating the need to process large amounts of training data in real-time and significantly reducing data processing time while maintaining prediction accuracy.
Solution Approach 2:
The patent uses partial action by training models on specific subsets of data rather than always using the complete dataset. Each model is trained on the minimum necessary data subset required for its specific function, reducing the overall data processing burden while still achieving sufficient prediction accuracy for each specific scenario.
3Adaptability or versatility
If complex models are used, then adaptability to changing conditions is improved, but computational resource requirements increase
Solution Approach 1:
The patent implements dynamics by making the model selection process adaptive and dynamic rather than static. The system can dynamically choose which pre-trained model to apply based on current shipping conditions, time periods, and data availability. This dynamic approach allows the system to maintain high adaptability to changing conditions while avoiding the continuous computational burden of using the most complex model for every scenario.
Data Source
AI summary
In variants, a method for predicting transit data can include, determining a set of models, training each model, determining package transit data, evaluating the set of models, selecting a model from the set of models, predicting package transit data and/or any other suitable element. In variants, the method can function to determine, select, and/or train one or more models to predict package transit (e.g., physical package delivery to a destination).