| Description |
1 online resource (152 p.) |
| Contents |
Cover -- Half Title -- Title -- Copyright -- Dedication -- Contents -- 1 Introduction -- 1.1 Review of Congestion Pricing: Domestic and International Implementations -- 1.2 Congestion Pricing in the United States -- 1.2.1 SR-91, San Diego, California -- 1.2.2 I-95, Miami, Florida -- 1.2.3 Atlanta, Georgia -- 1.2.4 District of Columbia -- 1.3 Congestion Pricing Strategies -- 1.4 Book Organization -- 1.5 Summary -- 2 Literature Review -- 2.1 Introduction -- 2.1.1 Dynamic Pricing Algorithm of I-95 Express (Phase 1) -- 2.2 I-95 Express Lanes -- 2.3 Performance Monitoring |
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2.4 Evaluation and Performance Measurement -- 2.4.1 Traffic Performance -- 2.4.2 Public Perception -- 2.4.3 Facility Users -- 2.4.4 System Operations -- 2.4.5 Environment -- 2.4.6 Transit -- 2.4.7 Economics -- 2.4.8 Land Usage -- 3 Congestion Pricing Models -- 3.1 Discrete Choice Analysis -- 3.2 Utility Theory -- 3.3 The Cell Transmission Model -- 3.3.1 Telecommuting Option -- 3.4 Solution Methodologies -- 3.5 Nonlinear Optimization Problems -- 3.5.1 Nonlinear Optimization Solution Methods -- 3.6 AMPL Software -- 3.7 General Description of Stochastic Approximation -- 3.7.1 FDSA Algorithm |
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3.7.2 Simulated Annealing -- 3.7.3 Genetic Algorithms -- 3.8 Tolling Methodology -- 3.8.1 Price Elasticity -- 3.8.2 Toll Algorithm -- 4 Model Formulation and Results -- 4.1 Objective Function -- 4.1.1 Akcelik Travel Time Function -- 4.1.2 BPR Travel Time Function -- 4.2 Problem Formulation -- 4.2.1 Basic Assumptions -- 4.3 Results and Sample Calculations -- 4.3.1 Simulated Annealing Example -- 4.3.2 Genetic Algorithm Example -- 4.3.3 FDSA Example -- 4.4 Application of Tolling Algorithm -- 4.5 Comparative Evaluation of GA, FDSA, and SA Algorithms -- 4.5.1 Vissim Simulation |
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4.5.2 Travel Times Data Collection -- 4.6 Analysis of Variance -- 4.6.1 Two-Way ANOVA -- 5 Data Collection and Demand Forecasting -- 5.1 Data Collection -- 5.2 Demand Forecasting -- 6 I-95 Lessons Learned -- 7 Congestion Charge Case Studies -- 7.1 The London Congestion Charge -- 7.1.1 Brief History of Congestion Pricing -- 7.1.2 What Causes Traffic Congestion? -- 7.1.3 Implementation -- 7.1.4 Operation -- 7.1.5 Boundary -- 7.2 The Stockholm Congestion Tax -- 7.2.1 Operation -- 7.2.2 Impacts and Lessons Learned (Stockholm Congestion Charge) -- 7.2.3 Seasonal Adjustments -- 7.2.4 Boundary |
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7.3 Singapore Congestion Fee -- 7.3.1 Singapore Congestion Charge-Operation -- 7.3.2 Singapore Congestion Fees-Impacts -- 7.3.3 Economic Impacts -- 7.3.4 Boundary -- 7.4 Milan Congestion Charge -- 7.4.1 Operation -- 7.4.2 Impacts -- 7.4.3 Payment -- 7.4.4 TelePass -- 7.4.5 Boundary -- Appendix A Travel Time Measurements -- Appendix B Akcelik Travel Time Constants -- Appendix C Matlab Output Plots -- Appendix D Value of Time Constants -- Appendix E Station Data I-95 Hot Lane -- References -- Index |
| Summary |
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 |
| Notes |
Description based upon print version of record |
| Subject |
Congestion pricing -- Data processing
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Congestion pricing -- Mathematical models
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Congestion pricing -- Mathematical models
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| Form |
Electronic book
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| ISBN |
9781003811800 |
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1003811809 |
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