ABSTRACT

Most American cities with substantial public transit ridership share a stark statistic: commuters on public transportation have disproportionately lower incomes than commuters who use automobiles. Previous research has also shown that higher income residents who use public transit typically rely on single-boarding trips, while lower-income individuals endure complex, lengthy trips, requiring several modes or transfers. Traditionally, transit agencies use quality of service (QoS) surveys to gauge passenger perceptions of performance. However, these surveys suffer important limitations that more often mask challenges faced by low-income residents with complex mobility experiences. In an attempt to address these gaps, several smartphone applications that allow residents to collect GPS-tagged, QoS data have been developed. While promising, these apps not only fail to collect critical information to characterize complex trips, but also lack privacy, transparency and decision support systems. This project will create novel methods, answer open empirical questions and provide research-based guidelines for the design, development, deployment and evaluation of a privacy-respectful toolkit to identify and characterize the multi-factorial challenges typical of complex trips often times endured by low-income residents; and to drive bottom-up, crowdsourced-informed actionable solutions via community conversations and a decision support system.

This interdisciplinary research effort will advance the state of the art in privacy, survey design, data analytics, transit equity, data-driven civic engagement, and transit simulations; and will create a unique opportunity to understand the interdependencies between these research areas generating new knowledge necessary to ultimately drive QoS transit improvements. By involving all relevant stakeholders in the project, and putting residents and transit equity at the center, this project propounds a more equitable and human-centered approach to smart cities, one in which technologies are not presented to residents, but rather designed with them to address articulated needs. To achieve this goal, this project will need to answer research questions organized along four research thrusts: (1) Understanding Participation: analysis of the privacy barriers that might prevent low-income residents from participating in mobility experience data collection efforts, how to lower them to sustain participation, and the QoS survey strategies that might provide a good balance between resident participation and quality data; (2) Mobility Experience Data Analysis: creation of novel, interpretable machine learning and statistical methods to identify and characterize transit challenges and equity from large-scale, high-dimensional, door-to-door mobility experiential data, and to do so in a way that is interpretable to all stakeholders; (3) Transparency for Civic Engagement and Solution Ideation: identification of the conditions, processes, tools and data needed to create democratic spaces where solutions to public transit challenges can be identified via transparent, data-driven, neighborhood conversations among all stakeholders involved: residents, advocacy groups and decision makers; and (4) Simulation-based Decision Support Systems based on transit QoS: creation of novel, interpretable simulations to identify the impact on city-scale transportation by incorporating local solutions to address specific community-identified-needs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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