Abstract
Freeways play a critical role in the transportation network. They also present several equity concerns for the users and communities they serve. Thus, it is critical to have a concrete understanding of the populations that use freeways. Currently, there is no methodology to determine this demographic profile for freeways or any other transportation network, nor are there data sets that capture this information empirically. This paper fills this literature gap by presenting a methodology utilizing ecological regression to estimate the demographic profile of freeways and other transportation networks. Ecological regression, also called ecological inference, allows for the extraction of individual-level characteristics from aggregate data sources. This makes it ideal for this situation because choosing to travel on a particular transportation network is based on individual-level characteristics such as income, car ownership, and so on. To complete this calculation, an ecological regression model must be built such that demographic data, which are often aggregated based on geography (i.e., census data), can be translated to capture the subset of the demographic profile that uses a specific transportation network. In this paper, Washington State’s Central Puget Sound regional freeway network is used to verify this methodology. The demographic profile of incomes for freeway users is calculated and compared with the demographic profile of incomes for the region. It was found that the income profile of freeway users is 0.5066% dissimilar to that of the entire population, indicating that freeway usage is essentially representative when assessing user income. This result is meaningful because it shows the effectiveness of this methodology for evaluating the demographics of critical transportation networks, which can further the study of transportation equity by providing a critical step for uniform equity metric quantification, which relies on understanding these critical demographics.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies. See Puget Sound Regional Council (2022) for data and https://github.com/sam-ricord/Estimating-a-Demographic-Profile-for-the-Central-Puget-Sound-Regionforcode.
Acknowledgments
The corresponding author would like to thank the fellow authors for their support in the creation of this work and paper, as well as those from the Puget Sound Regional Council who supported this work with critical data and insight, especially Kris Overby.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 3, 2022
Accepted: Apr 13, 2023
Published online: Jun 28, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 28, 2023
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