Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models

Abstract

Users of public transit networks require tools that generate travel plans to traverse them. The main issue is that public transit networks are time and space dependent. Travel plans depend on the current location of users and transit units, along with a set of user preferences and time restrictions. In this work, we propose the design and development of artificial intelligence (AI) planning models for engineering travel plans for such networks. The proposed models consider temporal actions, bus locations, and user preferences as constraints, to restrict the set of travel plans generated. Our approach decouples model design from algorithm construction, providing a greater level of flexibility and richness of solutions. We also introduce an integer linear programming formulation, and a fast preprocessing procedure, to evaluate the quality of the solutions returned by the proposed planning models. Experimental results show that AI planning models can efficiently generate close to optimal solutions. Furthermore, our analysis identifies user preferences as the most critical factor that increases solution complexity for planning models.