S-Curve + Delay for Bell Curve by Guy Lakeman
Guy Lakeman
Generation of Bell Curve from Initial Market through Delay in Pickup of Customers
This provides the beginning of an Erlang distribution model
The Erlang distribution is a two parameter family of continuous probability distributions with support . The two parameters are:
- a positive integer 'shape'
- a positive real 'rate' ; sometimes the scale , the inverse of the rate is used.
MATHS Statistics Physics Science Ecology Climate Weather Intelligence Education Probability Density Function Normal Bell Curve Gaussian Distribution Democracy Voting Politics Policy Erlang
- 10 months 1 week ago
PARKING through VANET
Abhinav Kapoor
The description of states are :
1. Cruising : State of cars which are moving out of parking area, but are still inside the parking lot.
2.Parked : State of cars which are already parked.
3. Just entered : State of cars which have just entered the parking lot and are searching for parking position.
- 7 years 7 months ago
Estacionamento
Simulação Computacional
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 7 years 5 months ago
Accident warning through VANET
Abhinav Kapoor
- 7 years 7 months ago
Clone of Parking Lot Problem (WIP)
David Joseph McLaren
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 7 years 5 months ago
Clone of Estacionamento
Igor Benić
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 6 years 1 week ago
Clone of Accident warning through VANET
Abhinav Kapoor
- 7 years 7 months ago
Clone of S-Curve + Delay for Bell Curve by Guy Lakeman
Ray Madachy
Generation of Bell Curve from Initial Market through Delay in Pickup of Customers
This provides the beginning of an Erlang distribution model
The Erlang distribution is a two parameter family of continuous probability distributions with support . The two parameters are:
- a positive integer 'shape'
- a positive real 'rate' ; sometimes the scale , the inverse of the rate is used.
MATHS Statistics Physics Science Ecology Climate Weather Intelligence Education Probability Density Function Normal Bell Curve Gaussian Distribution Democracy Voting Politics Policy Erlang
- 1 year 10 months ago
Clone of Accident warning through VANET
Abhinav Kapoor
- 7 years 7 months ago
Clone of Estacionamento
Alessandro Batista Martins
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 5 years 10 months ago
Clone of Clone of Parking Lot Problem (WIP)
Lynard Misa
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 6 years 3 months ago
Clone of PARKING through VANET
Mark Clements
The description of states are :
1. Cruising : State of cars which are moving out of parking area, but are still inside the parking lot.
2.Parked : State of cars which are already parked.
3. Just entered : State of cars which have just entered the parking lot and are searching for parking position.
- 5 years 5 months ago
Clone of Estacionamento
Jose Juan Hernandez
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 4 years 8 months ago
Clone of Estacionamento
Ray Madachy
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.
- 4 years 2 weeks ago
Clone of S-Curve + Delay for Bell Curve by Guy Lakeman
Rolf Widmer
Generation of Bell Curve from Initial Market through Delay in Pickup of Customers
This provides the beginning of an Erlang distribution model
The Erlang distribution is a two parameter family of continuous probability distributions with support . The two parameters are:
- a positive integer 'shape'
- a positive real 'rate' ; sometimes the scale , the inverse of the rate is used.
MATHS Statistics Physics Science Ecology Climate Weather Intelligence Education Probability Density Function Normal Bell Curve Gaussian Distribution Democracy Voting Politics Policy Erlang
- 9 months 2 weeks ago