Author ORCID Identifier

https://orcid.org/0000-0002-8803-3899

Year of Publication

2022

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Engineering

Department/School/Program

Civil Engineering

First Advisor

Dr. Gregory Erhardt

Abstract

Investments of public dollars on highway and transit infrastructure are influenced by the anticipated demands for highways and public transportations or traffic and transit ridership forecasts. The purpose of this study is to understand the accuracy of road traffic forecasts and transit ridership forecasts, to identify the factors that affect their accuracy, and to develop a method to estimate the uncertainty inherent in those forecasts. In addition, this research investigates the pre-pandemic decline in transit ridership across the US metro areas since 2012 and its influence on the accuracy of transit forecasts.

The sample of 1,291 road projects from the United States and Europe compiled for this research shows that measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Similarly for 164 large-scale transit projects, the observed ridership was about 24.6% lower than forecasts on average. The accuracy depends on the mode, length of the project, year the forecast was produced as well as socio-economic and demographic changes from the production to observation year.

In addition, we have found evidence of recent changes in transit demand to be affecting the transit ridership forecast accuracy. From 2012 to 2018, bus ridership decreased by almost 15% and rail ridership decreased by about 4% on average across the metropolitan areas in the United States. This decline is unexpected, because it coincided with the period of economic and demographic growth: indicators typically associated with rising transit ridership. We found that the advent of new mobility options in ride hailing services, bike and scooter shares as well as declining gas prices and increasing transit fares have the highest impact on ridership decline. Adjusting the ridership forecasts for these factors in a hypothetical scenario saw an improved transit ridership forecast performance.

Despite the advances in modeling techniques and the availability of rich travel data over the years, expecting perfect forecasts (where observations are equal to the forecasts), may not be prudent because of its forward-facing nature. Forecasts need to convey their inherent uncertainty so that planners and policymakers can take that into account when they are making any decision about a project. The existing methods to quantify the uncertainty rely on flawed assumptions regarding input variability and interaction and are significantly resource intensive. An alternate method is one that considers the uncertainty inherent in the travel demand models themselves based on empirical evidence. In this research, I have developed a tool to quantify the uncertainty in traffic and transit ridership forecasts through a retrospective evaluation of the forecast accuracy from the two largest available databases of traffic and transit ridership forecasts. The factors associated with the accuracy and the recent decline in transit ridership lead the formulation of quantile regression as a new method to quantify the uncertainty in forecasts. Together with a consideration of decision intervals or breakpoints where a project decision might change, such ranges can be used to quantify project risk and produce better forecasts.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2022.155

Funding Information

National Cooperative Highway Research Program (NCHRP), Transportation Research Board, Project 08-110, "Traffic Forecast Accuracy Assessment Research", 2017.

Transit Cooperative Research Program (TCRPP), Transportation Research Board, Project A-43, Recent Decline in Public Transportation Ridership: Analysis, Causes, Responses, 2021.

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