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A Road to Equity: Mitigating Bias in Transportation Infrastructure Planning

A Road to Equity: Mitigating Bias in Transportation Infrastructure Planning

Decorative: Downtown Detroit with orange and white construction barrels

To create sustainable and equitable transportation systems, decision-makers need access to accurate user data. Currently, the use of surveys in transportation planning suffers from inherent bias, often underrepresenting people of color and those with lower incomes, who are significant users of eco-friendly transportation like public transit, biking, and walking. This bias can lead to unnecessary road expansions and other misguided investments in infrastructure, diverting resources away from improving public transit, walking, and cycling options.

This research team, working with the Southeast Michigan Council of Governments (SEMCOG) and the Michigan Department of Transportation (MDOT), will analyze the biases in transportation survey data and propose alternative methodologies. Their goal is to produce a methodological guide to improve the integrity of transportation behavior data. They will conduct a quantitative investigation into national and state transportation household survey biases and a comprehensive comparison of sampling methods, instrument design, and post-processing corrections. They will focus on the Detroit metro area as a case study.

By systematically addressing biases in transportation data, the researchers aim to improve the accuracy of information used in infrastructure planning. Working with SEMCOG and MDOT ensures that their findings will inform the state of Michigan’s next phase of survey data collection starting in 2024. By sharing best practices, the project benefits will extend beyond southeast Michigan and guide transportation agencies nationwide toward more equitable and sustainable infrastructure planning.

Project team: Atiyya Shaw, PI (Civil and Environmental Engineering); Joe Grengs, Co-I (Urban and Regional Planning); Sunghee Lee, Co-I (Institute for Social Research)

This project received a $10,000 catalyst grant in Fall 2023.