Rollercoasters, Revenue, & Robots: How AI is Predicting Your Next Theme Park Throng (and Saving Some Serious Cash!)
Imagine running a bustling amusement park, complete with thrilling rides, splashy water slides, and delicious food stalls. Every day presents a new challenge: how many staff do you need to ensure everyone has a fantastic, safe experience without wasting money on too many employees? This isn't just a guessing game; it's a critical decision that impacts customer happiness and the park’s bottom line. This very dilemma faced Skara Sommarland, one of Sweden's biggest amusement parks. Their traditional methods for predicting visitor numbers were time-consuming, manual, and often unreliable, failing to consider enough important factors.
To tackle this, a fascinating project embarked on creating a decision support system (DSS) for Skara Sommarland. This innovative system aims to predict daily visitor numbers, enabling management to allocate the correct amount of personnel. The core of this system involved a showdown between two powerful prediction techniques: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR). The goal? To find a model that could predict visitors with at least 80% accuracy, a significant improvement over their existing methods.
The Problem: Riding the Staffing Rollercoaster
Skara Sommarland, open primarily during the summer months, sees an average of 255,500 visitors per season. A major portion of their expenses comes from personnel costs, accounting for about 23% of their revenue in 2016. Many of their employees are seasonal workers whose schedules are set based on management's visitor estimations.
The challenge is clear:
Understaffing leads to longer wait times, poorer service, and ultimately, lower customer satisfaction.
Overstaffing means unnecessary personnel costs, eating into the park's profitability.
Currently, Skara Sommarland relies on historical business data and weather forecasts, but these manual calculations are both slow and prone to error because they don't account for all the variables that truly influence visitor numbers. This highlighted the urgent need for a more accurate and efficient prediction model.
The Solution: A Digital Crystal Ball (Decision Support System)
A Decision Support System (DSS) is essentially a computer program designed to help people make better decisions by analyzing data and providing insights. For Skara Sommarland, the vision was to build a DSS that could display the predicted number of visitors, making staffing decisions easier for operational management. The most accurate prediction model would then be integrated into a user-friendly web application for daily use.
The Brains Behind the Predictions: AI vs. Traditional Stats
The project tested two main approaches for predicting visitor numbers:
Artificial Neural Networks (ANN): The Human Brain's Digital Cousin
Artificial Neural Networks (ANNs) are a form of machine learning inspired by how the human brain processes information. Think of it as a vast network of interconnected "neurons" (processing units) that learn by example rather than being explicitly programmed. ANNs are particularly adept at recognizing complex patterns and relationships in large datasets. They have proven successful in diverse fields like weather forecasting, stock market analysis, and image recognition. For this project, the hypothesis was that an ANN would be better at handling large amounts of varied data compared to traditional statistical methods.
Multiple Linear Regression (MLR): The Classic Calculator
Multiple Linear Regression (MLR) is a more traditional statistical method. It predicts an outcome (like visitor numbers) based on the assumption that there's a straightforward, linear relationship between the dependent variable (what you want to predict) and several independent variables (the factors influencing it). MLR models are widely used for predictions, from estimating adult height to forecasting company sales.
The project set out to investigate if the more advanced ANN would outperform the simpler MLR model, believing ANNs would be better equipped for the complexity of real-world visitor data.
The Blueprint: CRISP-DM – A Roadmap to Success
To ensure a structured and thorough development process, the project adopted the CRoss-Industry Standard Process for Data Mining (CRISP-DM) framework. This widely used methodology provides a clear lifecycle for data mining projects, guiding developers through key phases. It's a cyclical process, meaning knowledge gained in one phase can lead to revisiting earlier phases for refinement.
The CRISP-DM phases include:
Business Understanding: Defining the project's objectives and requirements from a business perspective, identifying goals for the data mining effort, and creating a project plan.
Data Understanding: Collecting initial data, describing its properties, exploring relationships, and verifying its quality.
Data Preparation: Cleaning, selecting, constructing new variables, integrating, and formatting the data to be suitable for modeling.
Modeling: Choosing and applying various modeling techniques (ANN and MLR in this case), generating test designs, building the models, and assessing their performance.
Evaluation: Assessing whether the models meet the business objectives and success criteria.
Deployment: Planning how the final model will be used by the end-users, including integration into a system and ongoing maintenance.
Gathering the Gold: What Data Fuels the Models?
The success of any prediction model hinges on the quality and relevance of the data it's trained on. For Skara Sommarland, a diverse range of historical data was collected:
Historical Business Data:
Number of visitors per day (the key variable to predict).
Number of camping and cabin bookings in the park's campsite, as this strongly correlates with park visitors.
Information on special events (e.g., concerts, large group bookings).
Financial reports, marketing costs, and ticket pricing.
Historical Weather Data: Collected from three nearby weather stations (Skara, Remningstorp, Hällum) by SMHI (Sweden's Meteorological and Hydrological Institute). This included:
Rainfall (total per day and per hour).
Cloud cover (hourly percentage).
Temperature (at 06:00 and 18:00).
Atmospheric pressure.
Wind strength.
Time Data: Categorical variables such as weekday, week number, month, and year.
Beyond these raw inputs, the project team skillfully constructed new variables to enhance the models' predictive power:
"Accommodation Total": Aggregated various campsite and cabin subcategories into a single, more consistent variable due to changes in how they were measured over time. This proved to have a strong correlation (0.79) with visitor numbers, explaining 63% of the variance.
Hourly averages for atmospheric pressure, wind strength, and cloud cover during park opening hours.
"Rain?": A binary (yes/no) variable indicating rainfall during opening hours (derived from hourly rainfall data), as rain during park hours is more impactful than overnight rain.
"With_Rain" and "Without_Rain": These "momentum" variables captured the effect of consecutive days with or without rain, hypothesizing that prolonged weather patterns could influence visitor decisions.
"SMA" (Sliding Moving Average): This variable built momentum based on the moving average of visitor numbers from the previous four days, helping to capture "strong" or "weak" periods in visitor trends.
Crucially, data quality was rigorously verified using six dimensions (accuracy, consistency, completeness, timeliness, uniqueness, validity), leading to the identification and resolution of various data issues.
Building the Beasts: How the Models Learned
After meticulously preparing the data (cleaning outliers, transforming values, integrating various sources), the models were built. To test their effectiveness, the holdout method with 10-fold cross-validation was used. This means the data was divided, with most used for training the models and a portion for testing their predictive ability. The primary measure for comparison was Mean Absolute Error (MAE), which indicates the average difference between predicted and actual values.
For the MLR model, several statistical assumptions needed to be met, leading to the removal of variables like "Year," "Week day," and "Wind Strength" due to lack of linear correlation or multicollinearity (where independent variables are too highly correlated with each other).
The ANN, on the other hand, required careful tuning of its "hyperparameters" – settings that define its structure and learning process. This was achieved using a "grid search" method, which systematically tests various combinations of settings to find the optimal configuration. This process was computationally intensive, taking about a week of runs to identify the best settings, including the number of layers, neurons, and learning rates.
The Grand Reveal: Who Won the Prediction Prize?
After all the data crunching and model building, the results were clear: the Artificial Neural Network (ANN) emerged as the superior prediction model.
The ANN achieved an impressive MAE of 737, while the MLR had a MAE of 856.
In terms of accuracy, the ANN reached approximately 79.82%, almost exactly hitting the project's data mining goal of 80%. The MLR, in comparison, achieved 76.56% accuracy.
The project concluded that the ANN successfully met the business objective, whereas the MLR fell slightly short. A key insight into why the ANN outperformed the MLR was its ability to handle "abnormalities" better. For example, the MLR performed poorly on days with zero campers (likely when the campsite was closed), leading to unreliable predictions. The ANN, being more adaptable to complex, non-linear relationships, was more robust in such scenarios. This demonstrated the ANN's strength in dealing with the varied and sometimes unpredictable nature of real-world data.
The Impact: Real-World Benefits for Skara Sommarland
The successful development of the ANN-powered DSS means tangible benefits for Skara Sommarland:
Accurate Staffing: Management can now use the daily visitor predictions to estimate employee needs more precisely.
Cost Reduction: By avoiding unnecessary overstaffing, personnel costs can be significantly lowered. Even a reduction of just one employee per day could lead to substantial savings over a season.
Improved Customer Satisfaction: Better staffing reduces the risk of understaffing, leading to shorter queues and a more enjoyable experience for visitors.
Time Savings: The automated system reduces the manual effort currently spent on estimations, freeing up management time.
The final product, a fully functional web application, allows Skara Sommarland's management to easily access and use these predictions in their daily operations. This application includes a user-friendly dashboard showing visitor forecasts for days ahead, along with weather forecasts, and features for data entry and user management. An API continuously gathers forecast weather data, and staff manually enter daily visitor numbers and bookings to keep the model updated and evolving.
Looking Ahead: Future Enhancements
While a significant success, the project identified several exciting avenues for future improvement:
Combining Models: Developing separate models for different scenarios (e.g., one for days with zero campers, another for peak season) could further enhance accuracy.
More Variables: Incorporating data from sources like Google Trends (searches for "Skara Sommarland" correlated strongly with visitors), weather forecasts (for longer-term planning), season ticket sales, and information about nearby events (like a big trotting race happening during peak visitor week) could make predictions even more robust.
Advanced AI Techniques: Exploring time series models like Recurrent Neural Networks (RNNs) that consider previous values of visitor numbers, or other machine learning models like Random Forest, could offer further refinements.
Conclusion
This project successfully demonstrated that an Artificial Neural Network (ANN) is an effective prediction model for estimating visitor numbers at amusement parks like Skara Sommarland. By leveraging historical business and weather data, combined with clever data engineering and the structured approach of CRISP-DM, the project created a Decision Support System that achieved its ambitious goal of 80% accuracy. This IT artifact has the realistic potential to improve park operations, leading to better staff allocation, reduced costs, and a more positive experience for every visitor. And because much of the data used is generic, this research offers a valuable blueprint for other amusement parks looking to unlock the power of AI to predict their own throngs and maximize their success.
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