Creating a simulation of how people moving around a site

Question

I am working on a project aiming to create a geographic simulation for how people interact around a site.

Situation: There are x people in an enclosed area (such as a zoo). There are y locations to visit (attractions).

Scenarios: I want to simulate how busy each location would get with the following decision-making approaches:

• When no location set, person goes to the next closest unvisited location
• When no location set, person goes to random unvisited location
• When no location set, person goes to recommended location (from app, interactive sign, etc)

Outcome: I want to know what leads to the best overall situation. In my case, I want to reduce overcrowding.

Is there any open source software, approaches I should consider for modeling this? I would prefer to do this in Python, though am open to any other options.

Answer 1

What you're talking about is agent-based modeling. ESRI has an entire extension for this which is open-source, but of course the ArcGIS platform it runs on isn't.

Answer 2

In addition to the aforementioned tools, agentscript is an interesting-looking library I just came across. It's written in CoffeeScript/JavaScript so it can run in a browser, on a node.js server, etc.

Wikipedia also has a Comparison of agent-based modeling software article.

Answer 3

Idrisi is very capable of this, it is less expensive than Arc stuff. You can even use python.

If you are just using python, perhaps a basic distance assigned to each site can be added to the data frame and used for the proximity value in Tobler's law.

I think using the spatial application of Tobler's law, where likelihood of visiting a site based on proximity would be an n over distance squared equation, would be a good start. Then, one could add parameters to the Tobler's law equation to represent the different situations presented. I don't know if you have the math already figured out but a coefficient for proximity, effectiveness of recommendations (maybe start with one - a perfect recommendation always adhered to) and a certain crowding factor where if location has more than n people it is not likely to get more.