  Existing parking recommendation solutions mainly focus on finding and
suggesting parking spaces based on the unoccupied options only. However, there
are other factors associated with parking spaces that can influence someone's
choice of parking such as fare, parking rule, walking distance to destination,
travel time, likelihood to be unoccupied at a given time. More importantly,
these factors may change over time and conflict with each other which makes the
recommendations produced by current parking recommender systems ineffective. In
this paper, we propose a novel problem called multi-objective parking
recommendation. We present a solution by designing a multi-objective parking
recommendation engine called MoParkeR that considers various conflicting
factors together. Specifically, we utilise a non-dominated sorting technique to
calculate a set of Pareto-optimal solutions, consisting of recommended
trade-off parking spots. We conduct extensive experiments using two real-world
datasets to show the applicability of our multi-objective recommendation
methodology.
