MoParkeR : Multi-objective Parking Recommendation
This addresses the need for more effective parking recommendations for drivers by moving beyond simple availability to handle dynamic, conflicting factors, though it is incremental as it builds on existing recommendation systems with a novel multi-objective formulation.
The paper tackles the problem of parking recommendation by considering multiple conflicting factors like fare, walking distance, and availability over time, proposing a multi-objective approach that uses non-dominated sorting to generate Pareto-optimal parking spot recommendations, with experiments on two real-world datasets demonstrating its applicability.
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.