Road congestion imposes major financial, social, and environmental costs. One solution is the operation of high-occupancy toll (HOT) lanes. This book outlines a method for dynamic pricing for HOT lanes based on non-linear programming (NLP) techniques, finite difference stochastic approximation, genetic algorithms, and simulated annealing stochastic algorithms, working within a cell transmission framework. The result is a solution for optimal flow and optimal toll to minimize total travel time and reduce congestion. ANOVA results are presented which show differences in the performance of the NLP algorithms in solving this problem and reducing travel time, and econometric forecasting methods utilizing vector autoregressive techniques are shown to successfully forecast demand. The algorithms discussed previously can be implemented and tested using the AMPL programming language. AMPL is described simply as a modeling language for mathematical programming. It has a comprehensive framework to model large-scale linear and non-linear optimization problems with vari­ables that are either discrete or continuous. AMPL is specifically suited to handle problems involving maximizing or minimization of algebraic expres­ sions subject to constraints expressed as inequalities. Other mathematical programming languages include LINDO, LINGO, CPLEX, and MPL.