Auto Shipping Price Prediction Using Dual ML Feedback Models
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Solution Overview
Problem
Existing automobile transportation pricing systems rely on static matrices and handcrafted rules that are difficult to update, leading to inaccurate and costly predictions due to market fluctuations, and lack automation capabilities.
Innovation Solution
A system utilizing two machine learning models, one to predict current prices and another to forecast price changes over time, dynamically adjusting predictions based on real-time data and user feedback to enhance accuracy and automate the transportation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static matrices and handcrafted rules are used for price prediction, then the system structure is simple, but the prediction accuracy deteriorates due to market fluctuations
Solution Approach 1:
The patent transitions from static pricing matrices to dynamic machine learning models that continuously learn from new data. The system automatically updates prediction algorithms based on real-time market conditions, replacing fixed rules with adaptive computational models that evolve with market fluctuations.
Solution Approach 2:
The system changes the parameters of the prediction model dynamically by training machine learning algorithms on historical and real-time data. Instead of fixed handcrafted rules, the model parameters (weights, biases, hyperparameters) are continuously optimized based on new market information, allowing the system to adapt to changing conditions.
2Ease of manufacture
If static matrices and handcrafted rules are used, then implementation is straightforward, but the system cannot be automated and requires continuous manual updates
Solution Approach 1:
The machine learning system performs self-updates by automatically training on new data without human intervention. The model autonomously learns from historical shipping data and real-time market conditions, eliminating the need for manual matrix updates and rule modifications while maintaining high prediction accuracy.
Solution Approach 2:
The system implements continuous feedback loops where prediction outcomes are monitored and fed back into the training process. Actual shipping prices and market conditions are compared with predictions, and the model parameters are adjusted based on this feedback, enabling automatic adaptation without manual reconfiguration.
3Reliability
If detailed price matrices are maintained, then comprehensive coverage is achieved, but maintenance complexity increases leading to delays
Solution Approach 1:
The patent extracts the essential patterns from comprehensive price matrices by training machine learning models on historical data. Instead of maintaining and updating entire detailed matrices, the system learns compact representations of pricing patterns that capture the essential relationships while reducing maintenance burden.
Solution Approach 2:
The manual mechanical process of updating price matrices is replaced with automated computational processes. Machine learning algorithms automatically process and learn from pricing data, substituting human-driven matrix maintenance with algorithmic learning that scales efficiently without proportionally increasing complexity.
4Ease of operation
If manual processes are used for price determination, then flexibility in handling edge cases is maintained, but automation is prevented and response time increases
Solution Approach 1:
The machine learning system autonomously handles edge cases by learning from historical examples of unusual shipping scenarios. The model automatically adapts to rare events and edge cases through pattern recognition in training data, eliminating the need for manual intervention while maintaining appropriate flexibility in handling diverse situations.
Data Source
AI summary
Methods, systems, and apparatuses are described herein for use of multiple machine learning models to improve automobile transportation cost prediction and transportation implementation. A computing device may generate two different trained machine learning models, with one configured to output a price prediction for automobile transportation and another configured to output a prediction of how an automobile transportation price will change over a time period. In response to receipt of a request for automobile transportation, the trained machine learning models might be used to generate a predicted price. Then, based on user activity, the predicted price and other transportation information may be transmitted to a server, and the machine learning models may be re-trained.