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research-article

Online Deep Ensemble Learning for Predicting Citywide Human Mobility

Authors:

Ryosuke Shibasaki,

Ritsu SakuramachiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 3

Article No.: 105, Pages 1 - 21

https://doi.org/10.1145/3264915

Published: 18 September 2018 Publication History

Abstract

Predicting citywide human mobility is critical to an effective management and regulation of city governance, especially during a rare event (e.g. large event such as New Year's celebration or Comiket). Classical models can effectively predict routine human mobility, but irregular mobility during a rare event (precedented or unprecedented), which is much more difficult to model, has not drawn sufficient attention. Moreover, the complexity and non-linearity of human mobility hinders a simple model from making an accurate prediction. Bearing these facts in mind, we propose a novel online gating neural network framework with two phases. In the offline training phase, we train a gated recurrent unit-based human mobility predictor for each day in our training set, while in the online predicting phase, we construct an online adaptive human mobility predictor as well as a gating neural network that switches among the pre-trained predictors and the online adaptive human predictor. Our approach was evaluated using a real-world GPS-log dataset from Tokyo and Osaka and achieved a higher prediction accuracy than baseline models.

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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
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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
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Cao ZZhao DSong HYuan HWang QMa HTong JTan C(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
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Index Terms

Online Deep Ensemble Learning for Predicting Citywide Human Mobility
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
  2. Ubiquitous and mobile computing
2. Social and professional topics
  1. Computing / technology policy
    1. Surveillance
      1. Governmental surveillance

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Published In

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 3

September 2018

1536 pages

EISSN:2474-9567

DOI:10.1145/3279953

Issue’s Table of Contents

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 the author(s) 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

New York, NY, United States

Publication History

Published: 18 September 2018

Accepted: 01 September 2018

Revised: 01 July 2018

Received: 01 May 2018

Published in IMWUT Volume 2, Issue 3

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Japan?s Ministry of Education, Culture, Sports, Science, and Technology (MEXT)

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Cited By

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
Cao ZZhao DSong HYuan HWang QMa HTong JTan C(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
https://doi.org/10.1109/TMC.2023.3301871
Bellodi EZese RPetrovich CFrascella ABertasi F(2024)Predicting the impact of public events and mobility in Smart CitiesIET Smart Cities10.1049/smc2.12087Online publication date: 3-Nov-2024
https://doi.org/10.1049/smc2.12087
Wang KPeng ZCai MWu HLiu LSun Z(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
https://doi.org/10.1016/j.physa.2024.129872
Huang SJi JWang YLi WZheng Y(2024)Development and validation of a soft voting-based model for urban fire risk predictionInternational Journal of Disaster Risk Reduction10.1016/j.ijdrr.2023.104224101(104224)Online publication date: Feb-2024
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Alasmari AFarooqi NAlotaibi Y(2024)Recent trends in crowd management using deep learning techniques: a systematic literature reviewJournal of Umm Al-Qura University for Engineering and Architecture10.1007/s43995-024-00071-3Online publication date: 20-Jun-2024
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Chen JWang HFan ZSong X(2024)How to Be a Well-Prepared Organizer: Studying the Causal Effects of City Events on Human MobilityAI, Data, and Digitalization10.1007/978-3-031-53770-7_4(42-64)Online publication date: 14-Mar-2024
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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
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Ushaa EVishal EKeerthna MVivek E(2023)Machine learning for detecting substance use behaviors from wearable biosensor data streams: A survey and outlook for 5G/6G connectivityi-manager’s Journal on Software Engineering10.26634/jse.18.1.2017718:1(56)Online publication date: 2023
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