Ride Control Simulation Using Stochastic Dispatch Events
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Solution Overview
Problem
Existing ride system control simulations fail to accurately account for stochastic events such as variable passenger load times, safety check times, and dispatch times, leading to inaccurate operation predictions and negative impacts on the ride system.
Innovation Solution
A simulation method that utilizes randomly selected values from data distributions to model stochastic events like passenger load times, safety check times, and dispatch times, incorporating physics-based models to simulate human interactions and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic simulation methods are used to model ride control systems, then the simulation model is simple and easy to implement, but the simulation accuracy is insufficient because stochastic events like variable passenger load times, safety check times, and dispatch times are not accounted for
Solution Approach 1:
The patent applies dynamics by transitioning from static deterministic simulation parameters to dynamic stochastic parameters. The simulation model incorporates random variables for passenger load times, safety check times, and dispatch times that vary across multiple simulation runs, allowing the system to adapt to different operational scenarios and accurately reflect real-world variability in ride control systems
Solution Approach 2:
The patent implements parameter changes by modifying simulation inputs from fixed deterministic values to stochastic distributions. By changing the nature of parameters from constant to randomly varying values drawn from probability distributions, the simulation captures the inherent uncertainty and variability in ride operations, thereby improving accuracy without requiring fundamental model restructuring
2Reliability
If stochastic events are incorporated into the simulation to improve operational predictions, then the accuracy of operation predictions is improved, but the complexity of the simulation increases
Solution Approach 1:
The patent applies feedback by using simulation results from multiple stochastic runs to refine and adjust simulation parameters and operational predictions. The aggregated data from numerous simulations with varying random inputs provides feedback on system behavior under different conditions, enabling more reliable predictions while managing complexity through statistical aggregation rather than deterministic complexity
3Measurement precision
If multiple simulation runs with randomly selected values are performed to account for variability, then the accuracy of simulating human interactions and environmental conditions is improved, but the computational time and resources increase
Solution Approach 1:
The patent applies partial or excessive action by performing a finite number of simulation runs that is sufficient to capture the essential variability and achieve acceptable accuracy, rather than attempting exhaustive simulation. This approach balances computational effort with the need to account for stochastic events, performing enough runs to achieve statistical significance without excessive computational overhead
Data Source
AI summary
In some implementations, a ride control simulation system may receive a request to simulate a ride control system controlling a movement of a passenger vehicle on a ride system, wherein the request includes vehicle information regarding the passenger vehicle and ride system information regarding the ride system. The ride control simulation system may execute, based on the request, a computer model to simulate the ride control system controlling the movement of the passenger vehicle on the ride system. Executing the computer model comprises randomly selecting values from one or more data distributions. The computer model is executed using the first value, the second value, and the third value as inputs. The ride control simulation system may cause an adjustment to an operation of the ride system based on a result of executing the computer model.