Fairness and Inclusivity in Urban Transportation Design Using Reinforcement Learning

Article link: https://alaworkshop2023.github.io/papers/ALA2023_paper_51.pdf

Abstract: Public transportation networks are the foundation of urban living. Designing transportation networks, however, is a complex task that involves physical, social, political, and legal constraints. This complexity is further compounded when considering the trade-off between efficiency and fairness. While efficient lines can boost ridership and reduce car dependency, thereby contributing to environmental sustainability, they may also prioritize densely populated central areas while neglecting other potentially underserved communities, exacerbating existing inequalities. It is therefore crucial to develop tools that address these challenges and prioritize fairness. Recent advancements in Artificial Intelligence offer promising solutions. In this presentation, I will showcase our work in using Reinforcement Learning to design public transportation networks. I will highlight the potential unfairness it can cause and propose strategies to mitigate them. Finally, I will introduce a conceptual framework aimed at fostering an inclusive design process that uses input from local communities and adapts its behaviour accordingly.

Presenter: Dimitris Michailidis

Date: 2024-02-27 15:00 (CET)

Online: https://bit.ly/ellis-hcml-rg