On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems
CoRR(2024)
Abstract
Research on the operation of mobility systems so far has mostly focused on
minimizing cost-centered metrics such as average travel time, distance driven,
and operational costs. Whilst capturing economic indicators, such metrics do
not account for transportation justice aspects. In this paper, we present an
optimization model to plan the operation of Intermodal Autonomous
Mobility-on-Demand (I-AMoD) systems, where self-driving vehicles provide
on-demand mobility jointly with public transit and active modes, with the goal
to minimize the accessibility unfairness experienced by the population.
Specifically, we first leverage a previously developed network flow model to
compute the I-AMoD system operation in a minimum-time manner. Second, we
formally define accessibility unfairness, and use it to frame the
minimum-accessibility-unfairness problem and cast it as a linear program. We
showcase our framework for a real-world case-study in the city of Eindhoven,
NL. Our results show that it is possible to reach an operation that is on
average fully fair at the cost of a slight travel time increase compared to a
minimum-travel-time solution. Thereby we observe that the accessibility
fairness of individual paths is, on average, worse than the average values
obtained from flows, setting the stage for a discussion on the definition of
accessibility fairness itself.
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