PathOracle: A Deep Learning Based Trip Planner for Daily Commuters

In this paper, we propose a novel data-driven approach for a trip planner, that finds the most popular multi-modal trip using public transport from historical trips, given a source, a destination, and userdefined constraints such as time, minimum switches, or preferred modes of transport. To solve the most popular trip and its variants, we propose a multi-stage deep learning architecture, PathOracle, that consists of two major components: KSNet to generate key stops, and MPTNet to generate popular path trips from a source to a destination passing through the key stops. We also introduce a unique representation of stops using Stop2Vec that considers both the neighborhood and trip popularity between stops to facilitate accurate path planning. We present an extensive experimental study with a large real-world public transport based commuting Myki dataset of Melbourne city, and demonstrate the effectiveness of our proposed approaches.