Balancing bike sharing systems (BBSS): instance generation from the
  CitiBike NYC data
  Bike sharing systems are a very popular means to provide bikes to citizens in
a simple and cheap way. The idea is to install bike stations at various points
in the city, from which a registered user can easily loan a bike by removing it
from a specialized rack. After the ride, the user may return the bike at any
station (if there is a free rack). Services of this kind are mainly public or
semi-public, often aimed at increasing the attractiveness of non-motorized
means of transportation, and are usually free, or almost free, of charge for
the users. Depending on their location, bike stations have specific patterns
regarding when they are empty or full. For instance, in cities where most jobs
are located near the city centre, the commuters cause certain peaks in the
morning: the central bike stations are filled, while the stations in the
outskirts are emptied. Furthermore, stations located on top of a hill are more
likely to be empty, since users are less keen on cycling uphill to return the
bike, and often leave their bike at a more reachable station. These issues
result in substantial user dissatisfaction which may eventually cause the users
to abandon the service. This is why nowadays most bike sharing system providers
take measures to rebalance them. Over the last few years, balancing bike
sharing systems (BBSS) has become increasingly studied in optimization. As
such, generating meaningful instance to serve as a benchmark for the proposed
approaches is an important task. In this technical report we describe the
procedure we used to generate BBSS problem instances from data of the CitiBike
NYC bike sharing system.
