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Activity Number: 72 - SPEED: Statistical Learning and Data Challenge Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 4:45 PM
Sponsor: Section on Statistical Computing
Abstract #323709
Title: Public Transit Policies to Promote Equitable Urban Mobility
Author(s): Jenny Y Huang* and Gaurav Rajesh Parikh and Albert Sun
Companies: Duke University and Duke Kunshan University and Duke University
Keywords: Transportation Policy; Causal Inference; urban mobility; Exact Matching; unequal commute; urban planning

Spatial mismatch is a widely-studied concept describing the mismatch between low-income communities and the places they work. A large obstacle to reaching jobs is commuting cost and reliable public transportation. Using granular public transit ridership data from King County Metro, we investigate how periods of route changes, fare suspension/reinstatement, and subsidies in Seattle, WA following the COVID-19 pandemic affect ridership, gaining insight on socioeconomic disparities in urban mobility patterns. We apply our analysis of Seattle data to identify how fare suspensions, subsidies, and incentive programs promote equitable access to public transport, extending recommendations to cities like Lansing, Baltimore, and Nashville. To analyze the effects of public transportation policies, we explore a Fast Large-scale Almost Matching Exactly (“FLAME”) algorithm for causal inference, a highly interpretable method for causal inference on observational datasets. Ultimately, we suggest that urban mobility and unequal commute data can give insight to policymakers on how to implement appropriate policies to make public transportation more accessible to specific racial and low-income groups.

Authors who are presenting talks have a * after their name.

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