Shipment Dwell Time Prediction Using Dynamic Route Data
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Solution Overview
Problem
Conventional techniques for predicting dwell time at shipment stops are limited by relying on simple historical averages, failing to account for varying conditions and resulting in inaccurate predictions.
Innovation Solution
A machine learning model trained on route and dwell time data is used to analyze shipment data and predict dwell times at stops, incorporating factors such as shipper, carrier, stop conditions, and operational details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple historical averages are used to predict dwell time, then the prediction method is simple and easy to implement, but the prediction accuracy is insufficient and does not account for varying conditions
Solution Approach 1:
The patent transforms the prediction approach from using simple historical averages to using multiple dynamic parameters including time of day, day of week, stop characteristics, route features, and environmental conditions. This parameter transformation enables the system to capture varying conditions that affect dwell time while maintaining computational feasibility through structured data organization and machine learning algorithms.
Solution Approach 2:
The patent implements dynamic prediction by continuously updating dwell time estimates based on real-time data and varying conditions. The system adapts to changing circumstances such as time of arrival, stop availability, weather conditions, and traffic patterns, rather than relying on static historical averages. This dynamic approach allows the prediction model to respond to current conditions while maintaining implementation feasibility through automated data collection and processing.
2Measurement precision
If machine learning models are used to predict dwell time, then prediction accuracy improves by accounting for varying conditions, but system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct functional components: data collection modules that gather information from multiple sources, data processing modules that clean and organize the data, machine learning model modules that perform predictions, and output modules that deliver results. This segmentation manages system complexity by creating modular, independently testable components while maintaining high prediction accuracy through comprehensive data analysis.
Solution Approach 2:
The patent introduces intermediary layers between raw data and final predictions, including data preprocessing pipelines that standardize inputs, feature engineering components that transform raw data into meaningful indicators, and model validation layers that ensure prediction reliability. These intermediaries manage complexity by creating structured transformation steps while enhancing prediction accuracy through systematic data preparation and validation.
Data Source
AI summary
Systems, methods, and computer-readable medium storing instructions for using a machine learning to predict dwell times for shipments along routes are described herein. The method, system, or instructions may include training the machine learning model using training data, storing the machine learning model in a memory, accessing route data of a shipment of interest, analyzing the route data using the machine learning model, and, based on the analyzing of the route data, outputting a predicted dwell time at a stop of the shipment of interest.