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Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes

Published: 04 August 2017 Publication History

Abstract

While exploring human mobility can benefit many applications such as smart transportation, city planning, and urban economics, there are two key questions that need to be answered: (i) What is the nature of the spatial diffusion of human mobility across regions with different urban functions? (ii) How to spot and trace the trip purposes of human mobility trajectories? To answer these questions, we study large-scale and city-wide taxi trajectories; and furtherly organize them as arrival sequences according to the chronological arrival time. We figure out an important property across different regions from the arrival sequences, namely human mobility synchronization effect, which can be exploited to explain the phenomenon that two regions have similar arrival patterns in particular time periods if they share similar urban functions. In addition, the arrival sequences are mixed by arrival events with distinct trip purposes, which can be revealed by the regional environment of both the origins and destinations. To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes. Essentially, the human mobility synchronization effect is encoded as a synchronization rate in the MHP; while the regional environment is modeled by introducing latent Trip Purpose and POI Topic to generate the Point of Interests (POIs) in the regions. Moreover, we provide an effective inference algorithm for parameter learning. Finally, we conduct intensive experiments on synthetic data and real-world data, and the experimental results have demonstrated the effectiveness of the proposed model.

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    cover image ACM Conferences
    KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2017
    2240 pages
    ISBN:9781450348874
    DOI:10.1145/3097983
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    Published: 04 August 2017

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    Author Tags

    1. hawkes process
    2. human mobility
    3. synchronization
    4. trip purpose
    5. variational inference

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    • National Key Research Program of China
    • University of Missouri Research Board
    • Natural Science Foundation of China

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    KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2024)Deep Adaptive Graph Clustering via von Mises-Fisher DistributionsACM Transactions on the Web10.1145/358052118:2(1-21)Online publication date: 8-Jan-2024
    • (2023)A Multi-View Approach for Regional Parking Occupancy Prediction with Attention MechanismsMathematics10.3390/math1121451011:21(4510)Online publication date: 1-Nov-2023
    • (2023)Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining PerspectiveACM Transactions on Knowledge Discovery from Data10.1145/356868317:5(1-24)Online publication date: 28-Feb-2023
    • (2023)COVID-19 is linked to changes in the time–space dimension of human mobilityNature Human Behaviour10.1038/s41562-023-01660-37:10(1729-1739)Online publication date: 27-Jul-2023
    • (2023)A geometry-driven neural topic model for trip purpose inferenceGeoInformatica10.1007/s10707-023-00504-628:2(313-333)Online publication date: 19-Aug-2023
    • (2023)Mobility trajectory generation: a surveyArtificial Intelligence Review10.1007/s10462-023-10598-x56:Suppl 3(3057-3098)Online publication date: 1-Dec-2023
    • (2022)Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory DataISPRS International Journal of Geo-Information10.3390/ijgi1102014011:2(140)Online publication date: 15-Feb-2022
    • (2022)A plug-in memory network for trip purpose classificationProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560969(1-12)Online publication date: 1-Nov-2022
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