LRS4DP: Location Recommendation System for Destination Prediction

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LRS4DP: Location Recommendation System for Destination Prediction
Title:
LRS4DP: Location Recommendation System for Destination Prediction
Journal Title:
2023 24th IEEE International Conference on Mobile Data Management (MDM)
Keywords:
Publication Date:
22 August 2023
Citation:
Zhao, B., Sun, H., Ng, W. S., Ka-Wei Lee, R., & Chen, Y. (2023). LRS4DP: Location Recommendation System for Destination Prediction. 2023 24th IEEE International Conference on Mobile Data Management (MDM). https://doi.org/10.1109/mdm58254.2023.00015
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2375-0324
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