research-article

Contextual conditional models for smartphone-based human mobility prediction

Authors:

Trinh Minh Tri Do,

Daniel Gatica-PerezAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 163 - 172

https://doi.org/10.1145/2370216.2370242

Published: 05 September 2012 Publication History

Abstract

Human behavior is often complex and context-dependent. This paper presents a general technique to exploit this "multidimensional" contextual variable for human mobility prediction. We use an ensemble method, in which we extract different mobility patterns with multiple models and then combine these models under a probabilistic framework. The key idea lies in the assumption that human mobility can be explained by several mobility patterns that depend on a sub-set of the contextual variables and these can be learned by a simple model. We showed how this idea can be applied to two specific online prediction tasks: what is the next place a user will visit? and how long will he stay in the current place?. Using smartphone data collected from 153 users during 17 months, we show the potential of our method in predicting human mobility in real life.

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Cited By

Ye ZZhang XChen XXiong HYu D(2024)Adaptive Clustering based Personalized Federated Learning Framework for Next POI Recommendation with Location NoiseIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312511(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312511
Zhong LZeng JWang ZZhou WWen J(2024)SCFL: Spatio-temporal consistency federated learning for next POI recommendationInformation Processing & Management10.1016/j.ipm.2024.10385261:6(103852)Online publication date: Nov-2024
https://doi.org/10.1016/j.ipm.2024.103852
Luo YYang XLi XChen ZLiu F(2024)Human emergency behaviour and psychological stress characteristic mining based on large-scale emergenciesComputational and Mathematical Organization Theory10.1007/s10588-024-09384-z30:4(293-320)Online publication date: 16-Feb-2024
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Index Terms

Contextual conditional models for smartphone-based human mobility prediction
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation

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Published In

cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 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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Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Ye ZZhang XChen XXiong HYu D(2024)Adaptive Clustering based Personalized Federated Learning Framework for Next POI Recommendation with Location NoiseIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312511(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312511
Zhong LZeng JWang ZZhou WWen J(2024)SCFL: Spatio-temporal consistency federated learning for next POI recommendationInformation Processing & Management10.1016/j.ipm.2024.10385261:6(103852)Online publication date: Nov-2024
https://doi.org/10.1016/j.ipm.2024.103852
Luo YYang XLi XChen ZLiu F(2024)Human emergency behaviour and psychological stress characteristic mining based on large-scale emergenciesComputational and Mathematical Organization Theory10.1007/s10588-024-09384-z30:4(293-320)Online publication date: 16-Feb-2024
https://doi.org/10.1007/s10588-024-09384-z
Spengler LGößwein EKranefeld ILiebherr MKracht FSchramm DGennat M(2023)From Modeling to Optimizing Sustainable Public Transport: A New Methodological ApproachSustainability10.3390/su1510817115:10(8171)Online publication date: 17-May-2023
https://doi.org/10.3390/su15108171
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: 16-Mar-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3252029
Li WShi XHuang DShen XChen JKobayashi HZhang HSong XShibasaki R(2023)PredLife: Predicting Fine-Grained Future Activity PatternsIEEE Transactions on Big Data10.1109/TBDATA.2023.33102419:6(1658-1669)Online publication date: Dec-2023
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Bieler MSkretting ABudinger PGronli T(2022)Survey of Automated Fare Collection Solutions in Public TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316160623:9(14248-14266)Online publication date: Sep-2022
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Shen XShi WChen PLiu ZWang L(2022)Novel model for predicting individuals’ movements in dynamic regions of interestGIScience & Remote Sensing10.1080/15481603.2022.202663759:1(250-271)Online publication date: 13-Jan-2022
https://doi.org/10.1080/15481603.2022.2026637
Altamrah AAlasmary WShuja JAlsaaban MAshraf I(2022)Intelligent Positioning System: Learning Indoor Mobility Behavior and Batch AffiliationsWireless Personal Communications: An International Journal10.1007/s11277-021-09010-0122:3(2521-2542)Online publication date: 1-Feb-2022
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Trivedi ASilverstein KStrubell EShenoy PIyyer M(2021)WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive SensingProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3460112.3471951(126-137)Online publication date: 28-Jun-2021
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