Neural Network Forecasting for Service Facility Utilization
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Solution Overview
Problem
Current methods for predicting the capacity utilization of transportation service facilities, such as gas stations or charging stations, rely on historical data, resulting in relatively imprecise forecasts, which hinder effective operational adjustments.
Innovation Solution
A method involving a neural network that receives and processes data about approaching vehicles to determine visit probabilities, allowing for precise adjustments to operating parameters like device activation, tariffs, and personnel allocation based on expected utilization, with continuous learning to improve forecast accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data analysis is used for forecasting utilization, then the method is simple to implement, but the forecast accuracy is relatively low
Solution Approach 1:
The patent replaces traditional mechanical/statistical forecasting methods with a neural network-based system. The neural network processes vehicle information (position, speed, route) and historical utilization data to predict future utilization with higher accuracy. This substitution of the forecasting mechanism resolves the contradiction by achieving superior measurement precision through a more sophisticated computational approach.
Solution Approach 2:
The patent introduces a neural network as an intermediary between raw data (vehicle information and historical utilization) and forecast results. This intermediary layer processes and synthesizes multiple data sources to generate accurate predictions, enabling high forecast accuracy while maintaining manageable system complexity through modular architecture.
2Productivity
If real-time adjustments are made based on accurate forecasts, then operational efficiency improves, but the complexity of monitoring and adjusting parameters increases
Solution Approach 1:
The patent performs preliminary forecasting of utilization before operational adjustments are needed. By predicting future utilization based on current vehicle data and historical patterns, the system prepares advance information that guides subsequent operational decisions. This preliminary action simplifies the adjustment process by providing pre-analyzed insights rather than requiring complex real-time analysis during execution.
Solution Approach 2:
The neural network system operates autonomously to generate forecasts and identify optimal adjustment parameters. The system self-monitors vehicle information and utilization patterns, automatically producing actionable insights without requiring complex manual monitoring procedures. This self-service capability improves operational efficiency while keeping the system manageable through automated decision-support functions.
Data Source
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AI summary
The present invention relates to a method, a computer program with instructions, and a device for operating a service facility for means of transportation. The invention further relates to a method and a computer program with instructions for training a neural network for use in such a method or in such a device. In a first step, information about means of transportation in the vicinity of the service facility is received (S1). Probabilities are determined for the means of transportation (S2) that they will visit a facility of the service facility. Based on the determined probabilities, at least one operating parameter of the service facility is adjusted (S3).