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A Comparative Evaluation of FDSA,GA, and SA Non-Linear Programming Algorithms and Development of System-Optimal Dynamic Congestion Pricing Methodology on I-95 Express

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Date Issued:
2013
Abstract/Description:
As urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for in- depth study of demand patterns and development of an appropriate pricing scheme which reduces congestion.This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution.We perform a comparative evaluation of FDSA, GA and SA non-linear programmingalgorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014.
Title: A Comparative Evaluation of FDSA,GA, and SA Non-Linear Programming Algorithms and Development of System-Optimal Dynamic Congestion Pricing Methodology on I-95 Express.
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Name(s): Graham, Don, Author
Radwan, Ahmed, Committee Chair
Abdel-Aty, Mohamed, Committee Member
Al-Deek, Haitham, Committee Member
Uddin, Nizam, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2013
Publisher: University of Central Florida
Language(s): English
Abstract/Description: As urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for in- depth study of demand patterns and development of an appropriate pricing scheme which reduces congestion.This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution.We perform a comparative evaluation of FDSA, GA and SA non-linear programmingalgorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014.
Identifier: CFE0005000 (IID), ucf:50019 (fedora)
Note(s): 2013-12-01
Ph.D.
Engineering and Computer Science, Civil, Environmental and Construction Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): dynamic pricing -- congestion pricing -- HOT lanes -- non-linear programming -- network -- stochastic approximation -- algorithms -- FDSA -- GA -- SA -- SPSA -- I-95 Express -- ANOVA -- Demand forecast -- Vector auto regression -- system-optimal
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005000
Restrictions on Access: public 2013-12-15
Host Institution: UCF

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