Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Civil Engineering

First Advisor's Name

Mohammed Hadi

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Albert Gan

Second Advisor's Committee Title

committee member

Third Advisor's Name

Xia Jin

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Wensong Wu

Fourth Advisor's Committee Title

committee member

Fifth Advisor's Name

L. David Shen

Fifth Advisor's Committee Title

committee member

Sixth Advisor's Name

Yan Xiao

Sixth Advisor's Committee Title

committee member

Seventh Advisor's Name

Priyanka Alluri

Seventh Advisor's Committee Title

committee member

Keywords

Freeway Operations, Ramp Metering, Ramp Signaling, Traffic Management, System Bottleneck, Intelligent Transportation Systems, Monte Carlo, Stochastic Capacity

Date of Defense

11-7-2017

Abstract

Ramp metering is an effective management strategy, which helps to keep traffic density below the critical value, preventing breakdowns and thus maintaining the full capacity of the freeway. Warrants for ramp metering installation have been developed by a number of states around the nation. These warrants are generally simple and are based on the traffic, geometry, and safety conditions in the immediate vicinity of each ramp (local conditions). However, advanced applications of ramp metering utilize system-based metering algorithms that involve metering a number of on-ramps to address system bottleneck locations. These algorithms have been proven to perform better compared to local ramp metering algorithms. This has created a disconnection between existing agency metering warrants to install the meters and the subsequent management and operations of the ramp metering. Moreover, the existing local warrants only consider recurrent conditions to justify ramp metering installation with no consideration of the benefits of metering during non-recurrent events such as incidents and adverse weather.

This dissertation proposed a methodology to identify the ramps to meter based on system-wide recurrent and non-recurrent traffic conditions. The methodology incorporates the stochastic nature of the demand and capacity and the impacts of incidents and weather using Monte Carlo simulation and a ramp selection procedure based on a linear programming formulation. The results of the Monte Carlo simulation are demand and capacity values that are used as inputs to the linear programming formulation to identify the ramps to be metered for each of the Monte Carlo experiments. This method allows the identification of the minimum number of ramps that need to be metered to keep the flows below capacities on the freeway mainline segment, while keeping the on-ramp queues from spilling back to the upstream arterial street segments. The methodology can be used in conjunction with the existing local warrants to identify the ramps that need to be metered. In addition, it can be used in benefit-cost analyses of ramp metering deployments and associated decisions, such as which ramps to meter and when to activate in real-time. The methodology is extended to address incidents and rainfall events, which result in non-recurrent congestion. For this purpose, the impacts of non-recurrent events on capacity and demand distributions are incorporated in the methodology.

Identifier

FIDC006590

ORCID

https://orcid.org/0000-0003-4064-1358

Available for download on Thursday, April 25, 2019

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