Calculating the Expected Number of People Arriving at a Restaurant in an Hour

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Discussion Overview

The discussion centers around calculating the expected number of people arriving at a restaurant in one hour, based on the flow of vehicles from two points on a highway. The analysis involves the Poisson distribution of vehicle arrivals, the types of vehicles, and the probability of stopping at the restaurant. The conversation explores the implications of these distributions and the assumptions involved in deriving the expected value of people arriving.

Discussion Character

  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant introduces a scenario involving vehicles arriving at a restaurant, specifying the Poisson distribution for vehicles from two points with different means and proportions of trucks.
  • Another participant suggests that the Poisson distribution can be simplified by using its mean, proposing a method to calculate the expected number of people arriving at the restaurant based on the number of vehicles.
  • A different participant agrees with the simplification but emphasizes the need to compute the expected number of people based on the vehicle types and their respective probabilities of carrying passengers.
  • Concerns are raised about assuming that the number of people arriving at the restaurant follows a Poisson distribution, with one participant asserting that this assumption cannot be made without further justification.
  • Another participant acknowledges the previous point, clarifying that while the vehicle arrivals are Poisson, the distribution of people arriving at the restaurant is not necessarily known.
  • One participant expresses understanding after the clarification, recognizing the need to focus on vehicle arrivals and their implications for calculating the expected number of people at the restaurant.

Areas of Agreement / Disagreement

Participants exhibit disagreement regarding the assumption that the number of people arriving at the restaurant follows a Poisson distribution. While some participants suggest that it might be reasonable to consider, others argue that this assumption cannot be made without further evidence. The discussion remains unresolved on this point.

Contextual Notes

Participants highlight the dependence on the Poisson distribution for vehicle arrivals and the associated probabilities for passenger counts in different vehicle types. There is an acknowledgment of the limitations in assuming the distribution of people arriving at the restaurant based solely on vehicle data.

jetoso
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Suppose we have a highway with vehicles coming from left(Point A) an from the right(Point B). And in some point we have a Restaurant.

The number of vehicles passing the point A in an hour follows the Poisson distribution with mean 60; 20% of these vehicles are trucks. The number of vehicles passing B in an hour is also Poisson with mean 80; 30% of these are trucks. In general, 10% of all vehicles stop at the restaurant. The number of persons in a truck is one; the number of passengers in a car is equal to 1, 2, 3, 4, or 5 with respective probabilities 0.30, 0.30, 0.20, 0.10 and 0.10.

Find the expected value E[Z] of the number of persons Z arriving at the restaurant within that on hour.

Any suggestions?
 
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I think you can ignore the poisson distribution here and just work with its mean. Otherwise (or if you must work it formally) it is somewhat more complicated, but intuitively I think you can.

So you find the number of people arriving at the restaurant passing point A, then the number of people arriving at the restaurant passing point B, then add them.

For point A you have 60 vehicles: 12 trucks and 48 cars. 10% of the trucks=1.2 trucks for 1.2 people. 10% of the cars=4.8 cars, so the # of people coming in cars is 4.8 * E[C] where C is the # of people in a car. Point B would be done similarly.
 
I think you are right. It may work since:
N(t) = n, the number of events up to time t is n.
P{N(t)=n} = exp(-lamba*t)(lamba*t)^n/n!
E[N(t)] = lamba*t
Z=N(t)
Then, E[Z]=lamba*t
Since t = 1 hout, then we can find E[Z] by computing the total number of people that arrive in 1 hour to points A and B, for trucks and cars respectively and then its 10% goes to the Restaurant.

Thanks.
 
You can't just assume that Z will also have the poisson distribution. I'm pretty sure it will, but you can't just assume that.
 
Well, if Z is the number of people arriving at each point A and B, and the arrivals have Poisson distribution, we can not think about Z as a Poisson r.v.?
 
You don't know that the arrivals have the Poisson distribution right off the bat. You only know that the vehicles have the Poisson distribution.
 
Oh, what wrong I am... you are right, finally understood the point. Thank you.
We only know arrivals of vehicles have a Poisson distribution with rate lamba vehicles/hour.
So, for this reason we only compute the arrival of vehicles, and considering the number of passenger for each type of vehicle we can compute the expected value of, say Z, the number of people who go to the restaurant. That's all. We do not know what distribution has the arrival of people to the restaurant, only the vehicles at points A and B.

Ok, now everything is clear. Thank you for your help. Sorry if I bothered you.
 

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