Shapeland Simulator

• Source code to accompany Defunctland's video "FASTPASS: A Complicated Legacy"
• Download the video at https://www.youtube.com/watch?v=9yjZpBq1XBE

License

• This source code is licensed under the Creative Commons 4.0 International License
• See the file named LICENSE for details

Tools You Will Need to Run The Simulation

The simulation is written in Python and has been tested with python 3.6.9. Download the latest version of python here: https://www.python.org/downloads/

The code also uses Jupyter Notebooks, available here: https://jupyter.org/install

Installation and Setup

Clone this repository to your local machine:

$ git clone https://github.com/TouringPlans/shapeland.git

Inside the repository is a directory called "Code". Start Jupyter Notebook like this and you'll see the entire notebook that runs the simulator and prints results:

$ jupyter notebook amusement_park_sim.ipynb

Code Organization

There are 5 main classes in this simulation:

• activity.py: An activity is something an agent can do inside the park. Activities include going on rides, eating, and so on.

• agent.py: Simulates one guest making decisions in the park.

• attraction.py: Encapsulates all of the calculations to simulate an attraction, including whether it has FASTPASS, its hourly capacity, how that capacity is split among different lines, and so on.

• behavior_reference.py: Each Agent has a behavioral archetype. -- Ride Enthusiast: wants to stay for a long time, go on as many attractions as possible, doesn't want to visit activites, doesn't mind waiting -- Ride Favorer: wants to go on a lot of attractions, but will vists activites occasionally, will wait for a while in a queue -- Park Tourer: wants to stay for a long time and wants to see attractions and activities equally, reasonable about wait times -- Park Visitor: doesn't want to stay long and wants to see attractions and activities equally, inpatient about wait times -- Activity Favorer: doesn't want to stay long and prefers activities, reasonable about wait times -- Activity Enthusiast: wants to visit a lot of activities, reasonable about wait times -- Archetypes can be tweaked and new archetypes can be added in behavior_reference.py.

• park.py: The park contains Agents, Attractions and Activities. -- Total Daily Agents: dictates how many agents visit the park within a day -- Hourly Percent: dictates what percentage of Total Daily Agents visits the park at each hour -- Perfect Arrivals: enforces that the exact amount of Total Daily Agents arrives during the day -- Expedited Pass Ability Percent: percent of agents aware of expeditied passes -- Expedited Threshold: acceptable queue wait time length before searching for an expedited pass -- Expedited Limit: total number of expedited pass an agent can hold at any given time