  In recent years, ridesharing platforms have become a prominent mode of
transportation for the residents of urban areas. As a fundamental problem,
route recommendation for these platforms is vital for their sustenance. The
works done in this direction have recommended routes with higher passenger
demand. Despite the existing works, statistics have suggested that these
services cause increased greenhouse emissions compared to private vehicles as
they roam around in search of riders. This analysis provides finer details
regarding the functionality of ridesharing systems and it reveals that in the
face of their boom, they have not utilized the vehicle capacity efficiently. We
propose to overcome the above limitations and recommend routes that will fetch
multiple passengers simultaneously which will result in increased vehicle
utilization and thereby decrease the effect of these systems on the
environment. As route recommendation is NP-hard, we propose a k-hop-based
sliding window approximation algorithm that reduces the search space from
entire road network to a window. We further demonstrate that maximizing
expected demand is submodular and greedy algorithms can be used to optimize our
objective function within a window. We evaluate our proposed model on
real-world datasets and experimental results demonstrate superior performance
by our proposed model.
