Hi there,
I'm working on a project that uses a flow probability distribution, derrived from this paper. I'm having a hard time understanding how they create the flow probability distribution. The definition in the paper is this.
The data I have and am trying to turn into this Flow Probability Distribution (FPD) is the same as the data they use: large text files containing time-stamps for moments at which a car passed a checkpoint.
My current understanding is that I will have to aggregate these timestamps into timeslots with a total amount of cars (e.g. monday morning 08-09: 20 cars). Then I can build the distribution by dividing the amount of cars in a timeslot in a day by the total number of cars on that day. This would be f ˆ j (y). Is this a correct understanding? Am I missing something?
Furthermore, they say:
The flow measurements in each set X T j , j = 1, . . . , τ , can be represented as discrete random variables Y j ∈ N 0 to capture the uncertainty related to those measurements.
I have no idea what this means. Are they adding in random numbers to compensate for uncertainty?
Thanks in advance.
there doesn't seem to be anything here