Modern amusement parks operate as complex technological ecosystems where artificial intelligence transforms both the creation and operation of rollercoasters. This solid improvement of the engineering processes extends from the initial design phase through daily maintenance operations, fundamentally changing how engineers approach the delicate balance between excitement and safety.

AI-Powered Design Innovation: Creating Better Coasters

Neural Network Spline Generation

Rollercoaster design has traditionally relied on human creativity and engineering intuition. Today, artificial intelligence generates track layouts that push boundaries beyond conventional human imagination. James St. Onge, creator of the YouTube channel Art of Engineering, developed a program that could create roller coasters by using ML. In the video, St. Onge’s program learned how to generate realistic splines from a neural network, use a physics engine for roller coasters, score each ride with a quantitative value and use an ML algorithm to optimize behavior. The program, CoasterAI, uses many roller coaster designs and runs simulations to determine what are the best blueprints.

Central to the AI roller coaster design process is the use of neural networks to generate track splines. A neural network is a computational model inspired by the arrangement of neurons in the human brain. These systems convert numerical inputs into precise angles for each track segment, creating smooth transitions that minimize jarring motions while maximizing excitement.

Physics Engine Integration

The design process involves sophisticated physics modeling that evaluates every aspect of the ride experience. The physics engine uses the law of conservation of energy to compute the roller coaster’s velocity at every node and considers rolling friction and aerodynamic drag to calculate losses. Additionally, it determines the radius of curvature to calculate centripetal acceleration and g-force.

This comprehensive physics simulation ensures that proposed designs remain within human tolerance limits while delivering optimal thrills. Engineers can test countless variations digitally before committing to physical construction.

Machine Learning Optimization

The genetic machine learning algorithm work in CoasterAI by creating a population of bots with randomized neural networks, evaluating their designs using a rating system, selecting the top-performing bots for the next generation, and repeating this process until an optimal solution is found. This evolutionary approach allows AI systems to discover novel design elements that might never occur to human designers.

The rating system quantifies ride quality based on multiple criteria including speed variations, g-force profiles, track steepness, and safety constraints. The rating system plays a crucial role in quantifying the quality of roller coaster designs. It assigns a numerical score to each track spline based on criteria such as speed, g-force, steepness, boundary constraints, and inversions.

Enhanced Safety Through AI Analysis

Motion Sickness Prediction

One breakthrough application addresses rider comfort through motion sickness prediction. Virtual Reality (VR) can cause an unprecedented immersion and feeling of presence yet a lot of users experience motion sickness when moving through a virtual environment. Rollercoaster rides are popular in Virtual Reality but have to be well designed to limit the amount of nausea the user may feel. This paper describes a novel framework to get automated ratings on motion sickness using Neural Networks.

These systems analyze ride characteristics such as maximum speed, average velocity, track length, and angle variations to predict potential motion sickness levels. The custom input parameters set consisted of maximum speed, average speed, total length, maximal downwards angle, maximal upwards angle, and type of rollercoaster. This capability allows designers to optimize layouts for broader rider appeal while maintaining safety standards.

Advanced Track Smoothing

AI spline generation tools via neural networks create ultra-smooth tracks that minimize jarring motions and enhance safety and comfort. These technologies will enable the design of increasingly complex and innovative layouts. This smoothing technology eliminates micro-bumps and irregularities that could cause discomfort or stress on ride vehicles.

Revolutionizing Maintenance Through Predictive Analytics

Sensor Networks and Data Collection

Modern rollercoasters operate within extensive sensor networks that monitor critical performance parameters continuously. Modern rollercoasters have many sensors that track everything from track vibrations to motor temperatures and ride speeds. These sensors generate large amounts of data daily, which AI systems analyze. The more data collected, the better the AI can detect patterns and predict potential mechanical failures.

Clients in the amusement park industry can monitor and manage data from all types of rides with retrofitted sensors for properties such as location, level, temperature, vibration, moisture, humidity or flow and with an alert when pre-determined thresholds or rules are met.

Machine Learning Algorithms for Maintenance

At the heart of these systems are advanced algorithms like machine learning (ML) and deep learning (DL). Machine learning models examine historical maintenance records, operational data, and real-time sensor readings to spot signs of wear and tear. Deep learning goes further by identifying complex patterns that traditional methods might miss, such as subtle performance changes that could signal an upcoming failure.

These algorithms continuously improve their accuracy as they process more operational data, becoming increasingly precise in their failure predictions over time.

Predictive Maintenance Implementation

Predictive maintenance is a key area where AI excels. Instead of relying on scheduled maintenance, which may miss early signs of trouble, AI offers real-time insights. When a rollercoaster part starts to deviate from its normal patterns, the AI system flags it, allowing maintenance crews to act before a failure occurs.

RideMinder allows ride operators to move from a repair and replace model to a predict and fix maintenance model using predictive analysis. This transformation represents a fundamental shift from reactive to proactive maintenance strategies.

IoT Integration and Real-Time Monitoring

Machine-to-Machine Communication

M2M stands for machine-to-machine communication and is used in IoT initiatives and other industrial control equipment. For roller coaster motor applications, it is often used to control large numbers of motors which must work in harmony to perform a function such as the launching of a ride vehicle.

This communication network enables coordinated control of complex ride systems while simultaneously collecting performance data for analysis.

Advanced Monitoring Systems

Industry leaders have developed sophisticated monitoring platforms specifically for amusement park applications. DMT, a TÜV NORD GROUP company, is launching the world’s first AI-supported real-time monitoring system for amusement rides, DMT RideGuard, just in time for the new amusement park season. This innovation aims to enhance safety and optimise maintenance processes.

“DMT RideGuard enables constant monitoring of our attractions in a way that has never been possible before. This feature of non-stop monitoring is a great advantage,” the company representatives say.

These systems provide continuous oversight that extends far beyond traditional inspection schedules.

Financial Impact and Return on Investment

Cost Reduction Through AI

One clear advantage is the reduction in maintenance costs. Traditional maintenance methods rely on scheduled inspections and reactive repairs, both of which can lead to excessive labor hours and unnecessary part replacements. With AI, maintenance is only performed when needed, which means fewer interventions and more efficient use of resources.

Predictive maintenance: IoT-enabled sensors monitor ride components, predicting failures before they happen, cutting downtime by up to 30% according to McKinsey & Company. By analyzing data trends, parks can service rides only when necessary, reducing maintenance costs and minimizing ride closure time.

Revenue Optimization

The financial benefits extend beyond reduced maintenance costs. For example, fewer breakdowns mean more consistent uptime, allowing parks to maximize ticket sales. Additionally, predictive maintenance helps avoid large-scale, costly repairs that could occur if a critical failure happens.

RideMinder predicts equipment performance issues so maintenance activities are scheduled at low-impact times, avoiding costly unplanned downtime. This strategic scheduling ensures maximum ride availability during peak operating periods.

Safety-Related Cost Avoidance

Another critical factor in the cost-benefit analysis is the avoidance of safety-related incidents. The cost of a safety failure—both in financial and reputational terms—can be astronomical. Lawsuits, lost revenue from park closures, and negative publicity can devastate a park’s financial health. AI’s ability to flag issues before they become safety risks can prevent these incidents, ensuring smoother operations and protecting the park’s bottom line.

Data Analytics and Decision Support

Operational Intelligence Platforms

AI-powered analytics platforms also play a vital role in turning raw data into actionable insights. These platforms provide park executives with easy-to-understand dashboards and reports, giving a clear overview of each ride’s health. With this information, decision-makers can allocate maintenance resources effectively, plan repairs during off-peak hours, and avoid the high costs of unexpected downtimes.

Guest Experience Enhancement

Beyond maintenance optimization, AI systems provide valuable insights into guest behavior and preferences. Using this complex data analytics system, Disney World was able to increase capacity by as much as 30%. In addition, their predictive machine learning algorithms are so precise, they can not only accurately predict where a guest will likely spend the majority of their time, they can also predict preferred dining times for dinner reservations and a child’s favorite Disney character amongst many other things.

Future Technological Integration

Virtual and Augmented Reality

One example is Disney parks utilizing the internet of things (IoT). In 2021, Disney unveiled MagicBands that guests can use around the resort. People use their IoT gadgets to enter the parks, redeem dining plan credits and unlock their hotel rooms on the resort grounds. These wearable devices create seamless integration between physical and digital experiences.

Smart Park Ecosystems

The IoT will transform maintenance in parks and venues when sensors transmit data to automated control systems. This enables interventions before hardware malfunctions or reaches the end of its useful life. It won’t just be a case of predicting the problem but of diagnosing the solution.

This evolution toward fully integrated smart park systems represents the next frontier in amusement park technology.

Implementation Challenges and Solutions

Technical Complexity

One of the main challenges is the lack of a dedicated AI program that can automatically design a 3D roller coaster from scratch. This means that designers need to think outside the box and explore alternative methods to utilize AI in their design process.

Current solutions focus on component-level optimization while researchers continue developing more comprehensive design systems.

Subjective Design Elements

One significant challenge is the subjective nature of evaluating roller coaster designs. Unlike other engineering disciplines, there is no single equation to define fun or objectively measure a roller coaster’s quality.

This challenge drives ongoing research into quantifying the human experience of thrill and excitement through measurable parameters.

Conclusion: The Future of Thrills

For amusement parks, the benefits of incorporating AI into rollercoaster maintenance are clear. Reduced downtime, lower maintenance costs, enhanced safety, and improved guest satisfaction are all within reach through the use of AI and data science. In an industry where every minute of ride uptime counts, AI provides a competitive edge, helping parks maximize revenue and deliver a seamless experience for visitors.

The integration of AI in the amusement park industry holds immense potential. As AI continues to advance, we can expect further advancements in coaster design, creating even more innovative and thrilling rides. The convergence of artificial intelligence, IoT sensors, and advanced analytics continues reshaping how engineers approach both the creative and operational aspects of rollercoaster development.

The transformation from traditional design methods to AI-assisted creation represents more than technological advancement—it embodies a fundamental shift toward data-driven decision making that enhances both safety and excitement. As these technologies mature, the boundary between human creativity and artificial intelligence will continue blurring, producing experiences that exceed what either could achieve independently.

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Sources: Computd, Art of Engineering

Written by Alius Noreika