Stochastic Processes - Poisson Process question

In summary, we can expect 8 express trains to arrive between 1:00-3:00pm if 10 arrived between 9:00-11:00am, the expected wait time for an express train is 5 minutes, the expected number of passengers on the downtown bus line in 12 hours is 2,304, and the probability of exactly 12 trains and buses arriving in 1 hour is approximately 0.1144.
  • #1
theCoker
9
0
I had this problem on my last midterm and received no credit for these parts.

1. Express trains arrive at Hiawatha station according to a Poisson process at rate 4 per hour, and independent of this, Downtown local buses arrive according to a Poisson process at rate 8 per hour.
a. Given that 10 Express trains arrive during the morning hours of 9:00-11:00 am, what is the expected number of Express trains that will arrive during the afternoon hours of 1:00-3:00pm
The attempt at a solution
We want E[# of trains]

lambdatrains=4
Given 9:00-11:00 => 10 trains
<=>\int_{9}^{11}4c dr = 10 => c=5/4
\int_{13}^{15}4c dr = ? = 10 <=> E[# of trains] = 10

Sorry, latex typesetting was not working.

b. Suppose your friend arrives at the station and decides to take the first transportation that arrives. Given she takes an Express train (it arrives first), what is the expected amount of time she waited for it to arrive?

The attempt at a solution
E[T]=1/lambdatrains=0.25=15minutes

c. Suppose each Downtown bus carries passengers, the number of which has a probability distribution with mean 24. Find the expected value of the number of passengers that ride the Downtown bus line during 12 hours.

The attempt at a solution
mu=24
lambdabus=8
E[# of passengers]=mu(P(T \leq 12))=24(1-exp(-12lambdabus)=24

d. What is the probability that a total of exactly 12 trains and buses arrive in a given 1-hour period?

The attempt at a solution
P(#trains=12 & #buses=12 | time = 1 hr)
average 4 trains per hour
average 8 buses per hour

Now, I am stuck.
 
Last edited:
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  • #2
Well, I got some help from elsewhere, but thought I might post the solutions anyway.

part a. solution
Expected number of trains from 1 to 3
=(2hrs) x (4 per hour) = 8 trains.

part b. solution
E[T]=1/(lambdatrains + lambdabuses)=1/12=5minutes.

part c. solution
(24 passengers/bus) x (8 buses/hour) x (12 hours)= 2,304 passengers.

part d. solution
P(N(t)=n) = [(lambda*t)^n][exp(-lambda*t)]/n!
P(N(1)=12) = [(12*1)^12][exp(-12*1)]/12! = 0.1144
 

Related to Stochastic Processes - Poisson Process question

1. What is a Poisson Process?

A Poisson Process is a type of stochastic process that models the occurrence of events over time. It is characterized by the assumption that the events occur independently and at a constant rate.

2. What are the key properties of a Poisson Process?

The key properties of a Poisson Process are:

  • The events occur independently of each other.
  • The events occur at a constant and known rate.
  • The number of events in non-overlapping time intervals are independent.
  • The probability of an event occurring in a small time interval is proportional to the length of the interval.

3. What is the Poisson distribution and how is it related to a Poisson Process?

The Poisson distribution is a probability distribution that describes the number of events that occur in a given time interval of a Poisson Process. It is related to a Poisson Process because the number of events in a fixed time interval follows a Poisson distribution if the process is in a steady state.

4. What are some real-world applications of the Poisson Process?

The Poisson Process has many real-world applications, including:

  • Modeling the number of customers arriving at a store or restaurant.
  • Modeling the arrival of phone calls at a call center.
  • Modeling the occurrence of earthquakes or other natural disasters.
  • Modeling the number of defects in a manufacturing process.

5. How is the Poisson Process different from other stochastic processes?

The Poisson Process differs from other stochastic processes in several ways:

  • It assumes that events occur independently and at a constant rate, while other processes may have different assumptions.
  • It is a counting process, meaning it counts the number of events that occur in a given time interval, while other processes may model different types of data.
  • It has specific properties, such as the number of events being independent and the probability of an event occurring in a small time interval being proportional to the length of the interval.

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