BALTO

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Image by Lucinda Levine

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 in Baltimore City; and to drive bottom-up, crowdsourced-informed actionable solutions via community conversations and a decision support system. 

To achieve this goal our team 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 for Transit QoS: creation of novel, interpretable simulations to identify the impact that local solutions – that address specific community-needs – have at city scale.