  Current transit suffers from an evident inequity: the level of service of
transit in suburbs is much less satisfying than in city centers. As a
consequence, private cars are still the dominant transportation mode for
suburban people, which results in congestion and pollution. To achieve
sustainability goals and reduce car-dependency, transit should be (re)designed
around equity. To this aim, it is necessary to (i) quantify the "level of
equity" of the transit system and (ii) provide an indicator that scores the
transit lines that contribute the most to keep transit equitable. This
indicator could suggest on which lines the transit operator must invest to
increase the service level (frequency or coverage) in order to reduce inequity
in the system.
  To the best of our knowledge, this paper is the first to tackle (ii). To this
aim, we propose efficient scoring methods that rely solely on open data, which
allows us to perform the analysis on multiple cities (7 in this paper). Our
method can be used to guide large-scale iterative optimization algorithms to
improve accessibility equity.
