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作者:

Zhao, Bing (Zhao, Bing.) | Sun, Haodong (Sun, Haodong.) | Ng, Wee Siong (Ng, Wee Siong.) | Ka-Wei Lee, Roy (Ka-Wei Lee, Roy.) | Chen, Yanyan (Chen, Yanyan.)

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EI Scopus

摘要:

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. © 2023 IEEE.

关键词:

Semantics Economic and social effects Trajectories Network architecture Telecommunication services Forecasting Deep learning Roads and streets Recommender systems Scalability Taxicabs Motor transportation Location based services Location

作者机构:

  • [ 1 ] [Zhao, Bing]Institute for Infocomm Research A*STAR, Singapore, Singapore
  • [ 2 ] [Sun, Haodong]Beijing University of Technology, Beijing Key Laboratory of Traffic Engineering, Beijing, China
  • [ 3 ] [Ng, Wee Siong]Beijing University of Technology, Beijing Key Laboratory of Traffic Engineering, Beijing, China
  • [ 4 ] [Ka-Wei Lee, Roy]Singapore University of Technology and Design, Information Systems Technology and Design, Singapore, Singapore
  • [ 5 ] [Chen, Yanyan]Beijing University of Technology, Beijing Key Laboratory of Traffic Engineering, Beijing, China

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ISSN: 1551-6245

年份: 2023

卷: 2023-July

页码: 11-20

语种: 英文

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