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 book compares different optimization approaches It presents case studies from around the world, such as the I-95 Express HOT Lane in Miami, USA Applications of Heuristic Algorithms to Optimal Road Congestion Pricing is ideal for transportation practitioners and researchers.
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