Date of Award

5-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Civil Engineering

Committee Member

Mashrur Chowdry, Committee Chair

Committee Member

Wayne Sarasua

Committee Member

Eric Morris

Abstract

Accurate estimation of annual average daily traffic (AADT) is critical in nearly every roadway decision, such as allocations of funding for roadway improvements and maintenance. While some roadway locations have permanent count stations capable of counting vehicles 24-hours a day throughout the entire year, they are typically only installed at selected locations on major roadways (i.e., freeways and major arterials) with high traffic volumes. On lower functional class roads and roadway segments on higher functional class roads without permanent count stations, short-term coverage counts are collected and adjusted with data from permanent count stations to estimate AADT. Short-term coverage counts are essential because they provide data from roadways of all functional classes and lane configurations, accounting for varying volumes on all roads maintained by an agency. Although necessary, coverage counts can be expensive and can exhaust resources such as investment in data collection workforce, equipment and data analysis. This study develops a strategy for estimating AADT on every roadway within a given jurisdiction using permanent count stations and short term coverage counts, while limiting the number of coverage counts needed. The goal of this thesis is to illustrate a noteworthy time and cost savings using a new centrality based AADT estimation method. A set of new deterministic variables, based on the theory of centrality, are introduced. This study revealed that estimated root mean square error (RMSE) for the new centrality based AADT method is half of the estimated RMSE in the travel demand based AADT model for the same area. Additionally, it was found that using centrality based AADT estimation model, the number of coverage count stations necessary can be reduced by more than 60% compared to the standard factor method for AADT estimation without compromising the AADT estimation accuracy.

Share

COinS