predict empirical probability distribution shift

Question

I'm looking into creating a simulation for passengers that arrive at a bus stop at a specific time of the day. I do this by drawing from a created probability distribution function. From measurements on bus ticket sales over the last 2 months I have derived the following pdf:

What I want to predict is what will happen if the number of passengers at this stop on average increases by lets say 10%. My first thought was to add 10% to every n.o. passengers that occur in the data, but what might occur is that in the extreme cases, more passengers than 25 could be at the bus stop.

My other alternative is to try and fit an existing distribution to the data and see what happens, but I don't think that this really represents my data very well.

What do you think is best way to approach this so that I can get an accurate distribution of the new situation?