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On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach

Published: 17 October 2018 Publication History

Abstract

Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models to predict destinations over a sub-trajectory, but their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. This paper presents a carefully designed deep learning model called TALL model for destination prediction. It not only takes advantage of the bidirectional Long Short-Term Memory (LSTM) network for sequence modeling, but also gives more attention to meaningful locations that have strong correlations w.r.t. destination by adopting attention mechanism. Furthermore, a hierarchical model that explores the fusion of multi-granularity learning capability is further proposed to improve the accuracy of prediction. Extensive experiments on Beijing and Chengdu real datasets finally demonstrate that our proposed models outperform existing methods without considering external features.

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    cover image ACM Conferences
    CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
    October 2018
    2362 pages
    ISBN:9781450360142
    DOI:10.1145/3269206
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 October 2018

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    Author Tags

    1. deep learning
    2. trajectory embedding
    3. trajectory prediction

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    • Research-article

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    • Open Program of State Key Laboratory of Software Architecture
    • National Natural Science Foundation of China
    • Australian Research Council discovery projects

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    Cited By

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    • (2024)Bayesian Estimation of Origin and Destination from Masked Trip Data2024 European Control Conference (ECC)10.23919/ECC64448.2024.10590859(3734-3739)Online publication date: 25-Jun-2024
    • (2024)Toward an accurate mobility trajectory recovery using contrastive learning基于对比学习的移动轨迹准确恢复Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230064725:11(1479-1496)Online publication date: 27-Dec-2024
    • (2024) F 3 VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery IEEE Transactions on Mobile Computing10.1109/TMC.2023.3301871(1-16)Online publication date: 2024
    • (2024)Modeling Route Representation With Mixed-Scale Hierarchical TransformerICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446095(5295-5299)Online publication date: 14-Apr-2024
    • (2024)Predicting trajectory destinations based on diffusion model integrating spatiotemporal features and urban contextsInternational Journal of Digital Earth10.1080/17538947.2024.242195517:1Online publication date: 30-Oct-2024
    • (2024)Coupling graph neural networks and travel mode choice for human mobility predictionPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2024.129872646(129872)Online publication date: Jul-2024
    • (2024)Transfer-learning-based representation learning for trajectory similarity searchGeoInformatica10.1007/s10707-024-00515-x28:4(631-648)Online publication date: 13-Apr-2024
    • (2024)TRoute: Dynamic Time-Dependent Route Recommendation on Road NetworksWeb Information Systems and Applications10.1007/978-981-97-7707-5_47(573-585)Online publication date: 11-Sep-2024
    • (2024)TOP: Taxi Destination Prediction Based on Trajectory Knowledge GraphWeb and Big Data10.1007/978-981-97-7235-3_21(311-326)Online publication date: 28-Aug-2024
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