[CSI Seminar Series]More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling

 

Topic: More Efficient Learning in Traffic Grids Viewed as Complex Adaptive Systems Using Agent Based Modeling

 

Presenter: Chuck Lane

 

Equation based modeling (EBM) is the most common form of scientific modeling. However, the creation of an appropriate EBM for a large system is likely to be very complicated and computationally expensive. In contrast, consider the result if we treat even a large system as a complex adaptive system (CAS), apply the basic tenets of a CAS, and model it using the concepts of agent based modeling (ABM). ABM requires only the definition of the model environment, the identification of key agents, and a minimum number of key behaviors of those agents. It is my contention that a CAS/ABM model when compared to a corresponding EBM model would be easier to design, implement, execute, and extend and that the results would still be predictive and useful.

Abstract:
My proposal is to create an ABM of the Uptown Charlotte, North Carolina traffic grid. Using traffic volumes based on actual Charlotte traffic counts, I will attempt to demonstrate that the grid with autonomous traffic signals will operate as a CAS. Finally, I will attempt to show that my CAS/ABM model will be more efficient to design and operate than a comparable EBM.

Date Published: 
Friday, September 9, 2016