On Extensibility and Personalizability of Multi-Modal Trip Planning

Abstract

Trip planning is a practical task that has drawn extensive attention from the AI planning and scheduling community and the industrial/commercial sectors. In this paper, we consider the setting of the multi-modal trip planning, where users can exploit different transportation modes, such as walking, biking, public transit, and taxi. In such a context, it would be of benefit if the user was able to extend the cost base, including traveling time and fare, of the planner, and to personalize the planner according to her own constraints and preferences. To this end, we designed and developed a hipergraph-based multi-modal trip planner that allows users to upload auxiliary cost metrics (e.g., crime rates), to specify constraints as a theory in the linear temporal logic, and to express preferences as a preferential cost function.