  This work reconsiders the concept of community-based trip sharing proposed by
Hasan et al. (2018) that leverages the structure of commuting patterns and
urban communities to optimize trip sharing. It aims at quantifying the benefits
of autonomous vehicles for community-based trip sharing, compared to a
car-pooling platform where vehicles are driven by their owners. In the
considered problem, each rider specifies a desired arrival time for her inbound
trip (commuting to work) and a departure time for her outbound trip (commuting
back home). In addition, her commute time cannot deviate too much from the
duration of a direct trip. Prior work motivated by reducing parking pressure
and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling
platform for community-based trip sharing could reduce the number of vehicles
by close to 60%.
  This paper studies the potential benefits of autonomous vehicles in further
reducing the number of vehicles needed to serve all these commuting trips. It
proposes a column-generation procedure that generates and assembles mini routes
to serve inbound and outbound trips, using a lexicographic objective that first
minimizes the required vehicle count and then the total travel distance. The
optimization algorithm is evaluated on a large-scale, real-world dataset of
commute trips from the city of Ann Arbor, Michigan. The results of the
optimization show that it can leverage autonomous vehicles to reduce the daily
vehicle usage by 92%, improving upon the results of the original Commute Trip
Sharing Problem by 34%, while also reducing daily vehicle miles traveled by
approximately 30%. These results demonstrate the significant potential of
autonomous vehicles for the shared commuting of a community to a common work
destination.
