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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Postgraduate and Professional
Illustrationen
6 s/w Photographien bzw. Rasterbilder, 17 s/w Zeichnungen, 17 s/w Tabellen, 23 s/w Abbildungen
17 Tables, black and white; 17 Line drawings, black and white; 6 Halftones, black and white; 23 Illustrations, black and white
Maße
Höhe: 216 mm
Breite: 140 mm
Dicke: 9 mm
Gewicht
ISBN-13
978-1-032-41566-6 (9781032415666)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Don Graham is a Data Scientist and Consultant. He served as data science professor at Northwestern University and transportation professor at Florida Institute of Technology for over ten years, in addition to working in industry at The Institute for Defense Analysis, Lockheed, and AVIS.
Autor*in
CapGemini Consulting, USA
1. Introduction. 2. Literature Review. 3. Congestion Pricing Models. 4. Model Formulation and Results. 5. Data Collection and Demand Forecasting. 6. I-95 Lessons Learned. 7. Congestion Charge Case Studies.
