research-article

Deep ROI-Based Modeling for Urban Human Mobility Prediction

Authors:

Ryosuke ShibasakiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 14, Pages 1 - 29

https://doi.org/10.1145/3191746

Published: 26 March 2018 Publication History

Abstract

Rapidly developing location acquisition technologies have provided us with big GPS trajectory data, which offers a new means of understanding people's daily behaviors as well as urban dynamics. With such data, predicting human mobility at the city level will be of great significance for transportation scheduling, urban regulation, and emergency management. In particular, most urban human behaviors are related to a small number of important regions, referred to as Regions-of-Interest (ROIs). Therefore, in this study, a deep ROI-based modeling approach is proposed for effectively predicting urban human mobility. Urban ROIs are first discovered from historical trajectory data, and urban human mobility is designated using two types of ROI labels (ISROI and WHICHROI). Then, urban mobility prediction is modeled as a sequence classification problem for each type of label. Finally, a deep-learning architecture built with recurrent neural networks is designed as an effective sequence classifier. Experimental results demonstrate that the superior performance of our proposed approach to the baseline models and several real-world practices show the applicability of our approach to real-world urban computing problems.

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Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

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Copyright © 2018 ACM.

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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Association for Computing Machinery

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 May 2017

Published in IMWUT Volume 2, Issue 1

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Cited By

Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
Zhang YLi TYuan YXu FYang FSun FLi Y(2024)Demand-driven Urban Facility Visit PredictionACM Transactions on Intelligent Systems and Technology10.1145/362523315:2(1-24)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3625233
Shen ZDu WZhao XZou J(2024)Retrieving Similar Trajectories from Cellular Data of Multiple Carriers at City ScaleACM Transactions on Sensor Networks10.1145/361324520:2(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3613245
Qin ZZhang PMa Z(2024)DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destinationIET Intelligent Transport Systems10.1049/itr2.1255418:10(1895-1909)Online publication date: 19-Aug-2024
https://doi.org/10.1049/itr2.12554
Ahmed IRaihan A(2024)Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging ChallengesMultimodal and Tensor Data Analytics for Industrial Systems Improvement10.1007/978-3-031-53092-0_7(125-166)Online publication date: 17-May-2024
https://doi.org/10.1007/978-3-031-53092-0_7
Yang CZhang ZFan ZJiang RChen QSong XShibasaki R(2023)EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic ControlIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.316538529:8(3586-3601)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3165385
Zhang XWang QYe ZYing HYu D(2023)Federated Representation Learning With Data Heterogeneity for Human Mobility PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325202924:6(6111-6122)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3252029
Michikata TTaniguchi SIshii THori CLin LHangli GKoshizuka N(2023)Multi-Horizon Visitor Forecasting for a Shopping Mall with Limited Historical Data Under Covid -19 Pandemic2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361307(0792-0798)Online publication date: 14-Nov-2023
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361307
Shi XZhang HChen QShibasaki R(2023)Few-shot count estimation of mobility dynamics by scaling GPSHandbook of Mobility Data Mining10.1016/B978-0-443-18428-4.00004-9(67-96)Online publication date: 2023
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Huang D(2023)Generative model for human mobilityHandbook of Mobility Data Mining10.1016/B978-0-443-18424-6.00002-7(95-115)Online publication date: 2023
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