An experience-based method for personalized routing

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.