Poisson Process: interevent times

In summary, we are looking at a one-way road with cars forming a Poisson Process with rate lambda cars per second. A pedestrian needs x/u seconds to cross the road and will only start crossing if she is certain that no cars will cross the pedestrian crossing while she is on it. The expected time until she completes the crossing is (ex/u - 1)/lambda, and we can use the "memoryless" property of exponential distributions to solve this problem. We can start by considering the amount of time (T) it takes for the pedestrian to start crossing, and under what conditions T = 0.
  • #1
SantyClause
5
0

Homework Statement



Consider a one-way road where the cars form a PP(lambda) with rate lambda cars/sec. The road is x feet wide. A pedestrian, who walks at a speed of u feet/sec, will cross the road if and only if she is certain that no cars will cross the pedestrian crossing while she is on it. Shwo that the expected time until she completes the crossing is (ex/u -1)/lambda


I know we need x/u seconds to cross, but I really don't even know how to start it :/ Will we use the erlang distribution?
 
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  • #2
SantyClause said:

Homework Statement



Consider a one-way road where the cars form a PP(lambda) with rate lambda cars/sec. The road is x feet wide. A pedestrian, who walks at a speed of u feet/sec, will cross the road if and only if she is certain that no cars will cross the pedestrian crossing while she is on it. Shwo that the expected time until she completes the crossing is (ex/u -1)/lambda


I know we need x/u seconds to cross, but I really don't even know how to start it :/ Will we use the erlang distribution?

What do you mean by the notation (ex/u -1)/lambda? Do you mean ##(e (x/u) - 1)/\lambda##, or ##(e^{x/u}-1)/\lambda## or ##e^{(x/u) - 1}/\lambda##, or something else?

Have you heard of the "memoryless" property of exponential distributions? You need to use it.

As to how to start: let T be the amount of time that passes until she starts crossing the road. Under what conditions is T = 0? If T > 0, she will wait for the next car to pass and then start again.
 

Related to Poisson Process: interevent times

1. What is a Poisson Process?

A Poisson Process is a mathematical concept used to model the occurrence of events over time. It is a type of counting process in which the events occur randomly and independently of each other. It is often used in fields such as statistics, physics, and engineering to model real-world phenomena.

2. What are interevent times in a Poisson Process?

Interevent times refer to the time intervals between successive events in a Poisson Process. In other words, it is the time that elapses between two consecutive occurrences of an event. These interevent times are assumed to follow an exponential distribution, which means they are independent of each other and have a constant average rate of occurrence.

3. How are interevent times calculated in a Poisson Process?

The interevent times in a Poisson Process can be calculated using the formula: T(n+1) - T(n) = -ln(U(n+1)), where T(n) is the time of the nth event and U(n+1) is a random number between 0 and 1. This formula is based on the exponential distribution, which is commonly used to model interevent times in a Poisson Process.

4. What is the relationship between the rate of occurrence and interevent times in a Poisson Process?

In a Poisson Process, the rate of occurrence of events is directly proportional to the average interevent time. This means that as the rate of occurrence increases, the average interevent time decreases. This relationship is known as the rate parameter (λ) and is an important factor in determining the characteristics of a Poisson Process.

5. What are some real-world applications of the Poisson Process to interevent times?

The Poisson Process and its interevent times have a wide range of applications in various fields, such as predicting traffic flow, modeling radioactive decay, and analyzing the occurrence of natural disasters. It is also commonly used in quality control and reliability studies to measure the time between product failures. Additionally, it has applications in finance, insurance, and telecommunications to model the arrival of customer service requests.

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