Author ORCID Identifier

https://orcid.org/0000-0003-4532-5864

Date Available

5-16-2022

Year of Publication

2022

Degree Name

Master of Science in Civil Engineering (MSCE)

Document Type

Master's Thesis

College

Engineering

Department/School/Program

Civil Engineering

First Advisor

Dr. Greg Erhardt

Abstract

Induced travel demand is the effect of increasing the amount of vehicle miles traveled because of an increase in roadway capacity. It is explained by the idea that increasing capacity makes driving on those roads more desirable, thereby causing more people to use them. In1962, Robert Downs postulated that “On urban commuter expressways, peak hour traffic congestion rises to meet maximum capacity,” referring to this as the law of peak hour traffic congestion. Since then, there have been ongoing debates about the effectiveness and environmental impact of roadway expansion projects, and efforts to quantify induced demand to inform those debates. Broadly, we observe that despite increasing roadway capacity over the past several decades, vehicle miles traveled (VMT) have increased faster, and congestion has worsened.

In this study I calculate how much of the increase in vehicle miles traveled in the United States can be attributed to increased lane miles. I find that between 1980 and 2019, total lane miles increased by 13%, resulting in 8% to 24% more VMT. I also find that population growth results in 41% more VMT and rising per capita incomes result in 19% more VMT, driving most of the increase in vehicle miles traveled. Other factors contribute 7%, with an important portion of the increase unexplained. These results suggest both that expanding roadway capacity will have only a modest effect on growing congestion, and that stopping capacity expansion projects will have only a modest effect on slowing VMT growth. This finding is important both to those who seek to mitigate growing traffic congestion, and to those who seek to limit the environmental impact of vehicular travel.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2022.213

clean_data.csv (265 kB)
Model Data

fac_data.csv (184 kB)
Factors Affecting Change Data

fac_calcs.xlsx (4097 kB)
Factors Affecting Change Calculation Spreadsheet

python_models.ipynb (14 kB)
Python Notebook to Run Model

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