  This paper studies the problem of allocating tasks from different customers
to vehicles in mobility platforms, which are used for applications like food
and package delivery, ridesharing, and mobile sensing. A mobility platform
should allocate tasks to vehicles and schedule them in order to optimize both
throughput and fairness across customers. However, existing approaches to
scheduling tasks in mobility platforms ignore fairness.
  We introduce Mobius, a system that uses guided optimization to achieve both
high throughput and fairness across customers. Mobius supports spatiotemporally
diverse and dynamic customer demands. It provides a principled method to
navigate inherent tradeoffs between fairness and throughput caused by shared
mobility. Our evaluation demonstrates these properties, along with the
versatility and scalability of Mobius, using traces gathered from ridesharing
and aerial sensing applications. Our ridesharing case study shows that Mobius
can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an
online manner.
