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Simulation

The objective of this course is to introduce students to fundamentals of simulation and its applications to transportation. If time allows, an intro to soft computing will also be presented. The objective of this course is to introduce students to fundamentals of simulation and its applications to transportation. If time allows, an intro to soft computing will also be presented.

 

 

Text Books and References

T1: Grinstead, Charles Miller, and James Laurie Snell. Introduction to probability. American Mathematical Soc., 2012.

T2:  Dekking, Frederik Michel. A Modern Introduction to Probability and Statistics: Understanding why and how. Springer Science & Business Media, 2005.

T3 (main text book): Jerry, Banks. Discrete-event system simulation. Pearson Education India, 1984.

T4: Hall, Randolph W. Queueing methods: for services and manufacturing. Pearson College Div, 1991.

T5: Leemis, Lawrence M., and Stephen Keith Park. Discrete-event simulation: A first course. Upper Saddle River, NJ: Pearson Prentice Hall, 2006.

Additional materials will also be distributed in the class.

 

Contents (Tentative)

Topic 1- Overview and application of simulation models

Topic 2- Recapping probability and statistics concepts

Topic 3- Queuing theory: analysis of steady-state and transient queuing systems  

Topic 4- Random number: linear congruential method; various tests of random numbers including Chi-square, Kolmogorov-Smirnov, run-test, and poker

Topic 5- Random variate generation: inverse transformation  

Topic 6- Input analysis: parameter estimation; goodness-of-fit tests

Topic 7- Validation and verification: practical guidelines to validation and verification; statistical tests of validity of outputs 

Topic 8- Output analysis: confidence interval estimation; reducing the dependency to initial condition

Topic 9- Multiple system comparison

Queuing Projects (If time permits)

Traffic simulation in AIMSUN (If time permits)

Prerequisites: 

The course does not have any specific pre-requisites but student are expected to be knowledgeable about statistical estimation and hypothesis testing.

Grading Policy: 

Tentative

Time: 

TBA

Term: 
Every odd semester
Grade: 
Graduate

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