Transport System Control Using Invertible Neural Trip Mapping
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Solution Overview
Problem
Existing transport systems face challenges in reliably controlling vehicle distribution due to unpredictable customer behavior and limited historical data, which can lead to inefficiencies and customer dissatisfaction.
Innovation Solution
A method using a neural network to perform an invertible mapping of transport trips to latent representations, generating synthetic trips that adhere to a predetermined distribution, and controlling the transport system with a trained control scheme based on these synthetic trips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical data is used to control the transport system, then the control can be based on real trip information, but the data base may hold too little data to allow reliable control, especially at the beginning of operation
Solution Approach 1:
The patent generates synthetic trip data that copies the statistical properties and patterns of real historical trip data. By creating artificial trip records with realistic distributions of trip durations, distances, and locations, the system obtains sufficient training data for reliable control even when actual historical data is limited.
Solution Approach 2:
The system performs preliminary generation of synthetic trip data before actual control operations begin. This advance preparation ensures that sufficient training data is available from the start of system operation, eliminating the problem of insufficient historical data at the beginning phase.
2Quantity of substance
If historical data is gathered over time to build a database, then more trip information becomes available, but customer behavior may change over time causing the database to become outdated
Solution Approach 1:
The system dynamically adjusts parameters of the synthetic data generation process to match current observed customer behavior patterns. By changing the statistical parameters (such as trip duration distributions, location preferences, time patterns) based on recent data, the synthetic trips remain current and reflective of evolving customer behavior.
Solution Approach 2:
The system uses feedback from actual observed trip data to continuously refine and update the synthetic trip generation process. The control system compares synthetic predictions with actual outcomes and adjusts the synthetic data generation parameters accordingly, ensuring the synthetic data remains aligned with current customer behavior.
3Adaptability or versatility
If the transport system adapts to customer trip patterns, then service quality improves, but the complexity of predicting and controlling trips increases
Solution Approach 1:
Instead of implementing complex real-time prediction and adaptation systems, the patent creates simplified synthetic trip data that copies the essential statistical patterns of customer behavior. This approach achieves adaptability through pre-generated representative data rather than through complex real-time processing and prediction algorithms.
Data Source
AI summary
Aspects concern a method for controlling a transport system comprising determining historical data of a multiplicity of transport trips performed by the transport system, training a neural network to perform an invertible mapping of transport trips to latent representations to fulfil, by the distribution of the latent representations of the multiplicity of transport trips, a predetermined fitting criterion with respect to a predetermined latent representation base distribution, sampling a multiplicity of latent representations from the base distribution, mapping each of the sampled latent representations to a respective transport trip by using the trained neural network to perform the inverse of the invertible mapping to generate a multiplicity of synthetic transport trips, determining a control scheme for the transport system using the multiplicity of synthetic transport trips and controlling the transport system using the determined control scheme.