Applications of Heuristic Algorithms to Optimal Road Congestion Pricin

This outlines a method for dynamic pricing of high-occupancy toll lanes based on non-linear programming techniques, finite difference stochastic approximation, genetic algorithms and simulated annealing stochastic algorithms, coupled with vector autoregressive techniques, producing a solution for optimal flows and optimal tolls.

List of contents

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.

About the author

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.

Product details

• Authors: Don (Capgemini Consulting Graham, Graham Don
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 30.01.2025
• EAN: 9781032415666
• ISBN: 978-1-0-3241566-6
• No. of pages: 142
• Subjects: Humanities, art, music > Art > Architecture; Natural sciences, medicine, IT, technology > Technology > Structural and environmental engineering; Social sciences, law, business > Political science > Political science and political administration; TECHNOLOGY & ENGINEERING / Civil / Highway & Traffic, TECHNOLOGY & ENGINEERING / Civil / General, ARCHITECTURE / Urban & Land Use Planning, TECHNOLOGY & ENGINEERING / Civil / Transportation, computer science, MATHEMATICS / Arithmetic, Urban & municipal planning, algorithms and data structures, Public Administration, Algorithms & data structures, Civil engineering, surveying & building, Urban and municipal planning and policy, Transport planning and policy, Transport planning & policy, Transport technology and trades, Civil engineering, surveying and building, Transport Technology & Trades, Public administration / Public policy