Transportation Route Forecasting for Empty Mile Reduction
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Solution Overview
Problem
Conventional resource allocation methods in transportation lack adaptability to real-time changes and future disruptions, leading to inefficiencies such as underutilized capacity, missed delivery windows, and increased operating costs, with inaccurate mileage forecasting hindering optimal route planning.
Innovation Solution
A system utilizing machine learning models to analyze historical data, segment journeys into inbound, outbound, and empty segments, identify the best performing model, and apply it to optimize routes, detect anomalies, and adjust forecasts dynamically to minimize empty miles and resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional manual planning or basic automated systems are used for resource allocation, then the system complexity is low, but the adaptability to real-time changes and future disruptions is poor
Solution Approach 1:
The patent replaces conventional manual planning or basic automated systems with machine learning models that automatically analyze historical data, segment journeys, and optimize routes. This substitution enables the system to adapt to real-time changes and future disruptions without requiring complex manual intervention, thereby improving adaptability while managing system complexity through automation.
Solution Approach 2:
The machine learning system performs self-optimization by automatically learning from historical data and adjusting routes based on performance metrics. The system segments journeys into inbound, outbound, and empty segments, then applies models to optimize each segment independently, enabling self-service optimization that improves adaptability without proportionally increasing overall system complexity.
2Productivity
If conventional resource allocation methods are used, then the ease of operation is maintained, but the productivity is reduced due to underutilized capacity and missed delivery windows
Solution Approach 1:
The patent segments transportation journeys into distinct segments (inbound, outbound, and empty segments) and applies different optimization strategies to each segment. This segmentation enables the system to improve resource utilization efficiency by optimizing each segment independently while maintaining operational simplicity through automated processing of each segment type.
Solution Approach 2:
The system uses performance metrics from historical data to train and validate machine learning models, creating a feedback loop that continuously improves route optimization. This feedback mechanism enhances productivity by learning from past performance while maintaining ease of operation through automated model application to new journeys.
3Measurement precision
If basic automated systems are used for mileage forecasting, then the device complexity is low, but the measurement precision is insufficient leading to suboptimal route planning
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical data before applying them to forecast mileage for new journeys. The models are validated against performance metrics to ensure accuracy before deployment. This preliminary training and validation process improves measurement precision while managing system complexity through pre-computed models rather than real-time complex calculations.
Solution Approach 2:
The patent uses machine learning models that copy and learn from patterns in historical data to predict future mileage. The models replicate successful routing patterns from the past, allowing the system to achieve high measurement precision by leveraging historical successes without requiring complex real-time analysis for each new journey.
Data Source
AI summary
System and methods for forecasting resource allocations are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical data associated with a transportation carrier, receiving, from the database, journey data associated with the transportation carrier, parsing and extracting, from the journey data, a plurality of segments, training a plurality of models based on the historical data, identifying a best model from the plurality of models based on performance metrics over a predetermined period of time, and applying the best model to the plurality of segments to identify an optimal route associated with the transportation carrier.