A year ago I have shared recording of my lecture in which I have used Matlab and Markov chains to solve board game Monopoly. The solution revolved around finding properties, which were visited most often.

The following youtube video discusses a different approach based on machine learning.

Typically due to friction (or other dissipative forces) physical systems reach stationary states, even if it takes really long time. Nonlinear systems, on the other hand, sometimes exhibit strange and erratic behavior.

Last time we have looked at how four different parking strategies work when inflow of drivers is homogeneous. We have done similarly to what was done in [1, 2], though our angle was a bit different.

This time we have modified the approach further. Namely, now we allow you to randomize the inflow of drivers and explore how do the performance of these strategies change in nonhomogeneous society.

A quick video about stop-and-go waves and how they arise and how to prevent them. Spoiler: the solution is not that original.

Traffic problems are ones we all face every day and we can explore using Physics of Risk tools such as agent-based models. Here we will take a look at a couple simple parking strategies from [1, 2].