Keywords

Machine learning, Mathematical optimization, Road work zones -- Florida -- Safety measures

Abstract

Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners as they are required to plan vehicle routes around construction zones in such a way that maximizes the safety of construction workers and reduces the impact on daily commuters. This research aims at developing a framework for optimizing the planning of construction detours. The main objectives of the research are to first identify all the decision variables that affect the planning of construction detours and secondly, implement a model based on shortest path formulation to identify the optimal alternatives for construction detours. The ultimate goal of this research is to offer construction planners with the essential guidelines to improve construction safety and reduce construction zone hazards as well as a robust tool for selecting and optimizing construction zone detours.

Notes

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Graduation Date

2011

Semester

Spring

Advisor

Khalafallah, Ahmed

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Format

application/pdf

Identifier

CFE0003586

URL

http://purl.fcla.edu/fcla/etd/CFE0003586

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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