LRS4DP: Location Recommendation System for Destination Prediction

Destination prediction based on the partial trajectory of a moving vehicle is vital for urban mobility applications. Recent research efforts focus on improving the prediction accuracy by incorporating more spatio-temporal semantics through complex model architectures, which inevitably impact the generalization and scalability due to ad-hoc hyper-parameters and heavier computations. In the present study, we propose a novel Location Recommendation System for Destination Prediction, LRS4DP. Through an integrated design of several technologies (map-matching, deep learning and recommender system), LRS4DP provides an end-to-end solution for destination prediction based on input trajectories and road network configurations. By adopting a node-based spatial discretization scheme through map-matching, LRS4DP is able to adapt according to the local road network density and generalize to different urban layouts. As compared to the state-of-the-art algorithms, our proposed Top-K formulation based on individual road nodes leads to fundamentally better spatial precision and prediction accuracy even with simple model architectures. We further designed the offline training and online serving as a location recommendation system to achieve better scalability and flexible trade-off between performance and run-time. The experimental evaluation of two real-world taxi datasets demonstrates the generalization of LRS4DP under different urban scales and layouts. The LRS4DP framework is also generically applicable for location prediction tasks (e.g., next location and passing-by location predictions) and capable to support various downstream transportation and location-based service applications.