Satisfying user preferences in optimised ridesharing services:

摘要

Ridesharing services offer on-demand transportation solutions while improving the utilization of the available capacity of the vehicles on the road. Profit, travel time, and cost are the commonly optimized objectives in current ridesharing services. Existing multi-objective optimization-based services mainly focus on maximizing profit for service providers while minimizing the travel time for passengers. However, various personal preferences (e.g., co-riders’ gender for a passenger or preferred area of service for a driver) should be considered when offering such services. Such preferences are often conflicting with one another and with the objectives such as cost and travel time. Therefore finding an optimized solution and satisfying such preferences simultaneously is challenging. To address this challenge, this paper proposes a Multi-agent, Multi-objective, Preference-based ridesharing model (MaMoP) that offers an optimized ridesharing solution while satisfying users’ preferences simultaneously. MaMoP uses social-reasoning techniques to model preferences and their relations and employs evolutionary algorithms to find an optimized solution.