Abstract:
Navigation apps use transportation recommendation. Previous transportation recommendation solutions only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and ignore situational context, resulting in poor user experience. We propose $\mathsf {Hydra}$, a multi-task deep learning-based recommendation system for multi-modal transportation planning that adapts to various situational contexts (e.g., nearby point-of-interest (POI) distribution and weather). We use existing routing engines and big urban data to design a two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes and heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs, and transportation modes from user implicit feedbacks, which captures their collaborative transportation mode preferences. We propose two models to recommend the best uni-modal and multi-modal transportation routes: a light-weight gradient boosting decision tree (GBDT) model and a multi-task wide and deep learning (MTWDL) model. We optimize the framework for real-time, large-scale route query and recommendation.
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