AM-84 - Simulation modeling

Learning Objectives: 
  • Conduct an experiment using simulation techniques from an activity perspective
  • Explain how a simulation from an activity perspective can be used in transportation
  • Discuss important computational laboratory tools for creating new models and visualizing model simulations and model outcomes
  • Discuss whether, when prior information is absent, repeatedly generating random synthetic datasets can be used to provide statistical significance
  • Discuss Monte Carlo simulation use in GIS&T
  • Discuss effective scientific use of supervisory genetic algorithms with agent-based simulation models
  • Describe how supervisory search and optimization methods can be used to analyze key characteristics of initial conditions and results and to optimize results based on systematic targeted search through the parameter and random seed spaces