Machine Learning Model for Freight Cost Prediction
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Solution Overview
Problem
Entities face challenges in accurately predicting freight shipping costs due to complexities in calculating costs, seasonal variations, and short-term conditions such as fuel costs, weather, and natural disasters, making it difficult to estimate costs without advanced knowledge of freight service calculations.
Innovation Solution
A machine learning model is developed to predict freight shipping costs by generating a training dataset from historical data, including features like weight, distance, and time intervals, and using an ensemble method like the random forest algorithm to provide accurate predictions, with the ability to retrain based on updated datasets to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional freight cost calculation methods are used, then the calculation process is simple and transparent, but the prediction accuracy is low due to inability to account for seasonal variations and short-term conditions
Solution Approach 1:
The patent replaces traditional mechanical calculation methods with a machine learning-based predictive system. The machine learning model processes historical freight data, seasonal patterns, and external factors to generate cost predictions, substituting manual or algorithmic calculations with an automated AI system that learns from data patterns to improve prediction accuracy.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw freight data and cost predictions. This intermediary component processes and interprets complex relationships in the data, translating historical patterns and external factors into accurate cost estimates without requiring direct complex calculations from the user.
2Reliability
If a machine learning model is used to predict freight costs, then the prediction accuracy improves by accounting for seasonal and short-term variations, but the system complexity increases
Solution Approach 1:
The patent performs preliminary actions by training the machine learning model on historical freight data before actual predictions are made. The model learns patterns from past performance, seasonal variations, and external factors during the training phase, enabling accurate predictions without requiring complex real-time calculations during actual freight operations.
Solution Approach 2:
The machine learning model performs self-service by automatically learning from historical data and improving its predictions over time without requiring manual intervention. The system self-updates its understanding of freight patterns and external factors, reducing the need for human expertise in complex calculations while maintaining high prediction accuracy.
3Adaptability or versatility
If historical data from multiple time intervals is used for training, then the model captures seasonal and short-term patterns, but the data processing complexity increases
Solution Approach 1:
The patent segments historical data into different time intervals (long-term and short-term patterns) and processes them separately through the machine learning model. This segmentation allows the model to capture distinct patterns from different time periods without overwhelming complexity, organizing data into manageable categories for systematic analysis.
Solution Approach 2:
The patent changes parameters such as time intervals, data granularity, and model complexity based on the specific analytical needs. By adjusting these parameters, the system can focus on long-term seasonal patterns or short-term variations as required, adapting the data processing complexity to match the specific adaptability needs without unnecessary computational overhead.
Data Source
AI summary
Provided is a system to generate a machine learning model to provide a predicted cost associated with shipping a shipment of freight from an origin of the shipment of freight to a destination of the shipment of freight based on a training dataset and a predicted distance and to determine the predicted cost associated with shipping a specified shipment of freight from the origin of the specified shipment of freight to the destination of the specified shipment of freight using the machine learning model. The training dataset includes data associated with shipments of freight conducted during a first time interval and data associated with shipments of freight conducted during a second time interval. The first time interval is at least a year and the second time interval is shorter than the first time interval. A computer implemented method and computer program product are also provided.