Kişiselleştirilmiş yönlendirme için deneyime dayalı bir yöntem

Abstract

Navigation devices that are tailored to the user's preferences offer personalized routes. When multiple users are involved, it can be hard to find a route that suits everyone's preferences and avoid conflicting interests. A decision support system can improve the quality of user decisions. Traditional systems typically consider only the predefined preferences of one user or a group with similar preferences. This study aims to develop a decision support system for a group of people with diverse preferences, using a method that considers their experiences regarding time and space. The method utilizes IoT, agent-based modeling, multi-objective optimization, and crowdsourced data to create a personalized navigation system for a group, such as a family car, that considers each group member's preferences. The study uses simulation to demonstrate how this method can be applied, and it is created using Grasshopper for Rhino and add-ons. The main original contribution of this research is to show how social aspects can be incorporated into personalized navigation systems for a heterogeneous group. The major challenge was the data-sharing policies.