  Accurately predicting the real-life performance of algorithms solving the
Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems
with ridesharing requires evaluating them on representative instances. However,
the benchmarking of state-of-the-art DARP solution methods has been limited to
small, artificial instances or outdated non-public instances, hindering direct
comparisons. With the rise of large MoD systems and the availability of open
travel demand datasets for many US cities, there is now an opportunity to
evaluate these algorithms on standardized, realistic, and representative
instances. Despite the significant challenges involved in processing obfuscated
and diverse datasets, we have developed a methodology using which we have
created a comprehensive set of large-scale demand instances based on real-world
data. These instances cover diverse use cases, one of which is demonstrated in
an evaluation of two established DARP methods: the insertion heuristic and
optimal vehicle-group assignment method. We publish the full results of both
methods in a standardized format. The results show significant differences
between areas in all measured quantities, emphasizing the importance of
evaluating methods across different cities.
