research-article

The influence of temporal and spatial features on the performance of next-place prediction algorithms

Authors:

Wilhelm Kleiminger,

Silvia SantiniAuthors Info & Claims

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

Pages 449 - 458

https://doi.org/10.1145/2493432.2493467

Published: 08 September 2013 Publication History

Abstract

Several algorithms to predict the next place visited by a user have been proposed in the literature. The accuracy of these algorithms -- measured as the ratio of the number of correct predictions and the number of all computed predictions -- is typically very high. In this paper, we show that this good performance is due to the high predictability intrinsic in human mobility. We also show that most algorithms fail to correctly predict transitions, i.e. situations in which users move between different places. To this end, we analyze the performance of 18 prediction algorithms focusing on their ability to predict transitions. We run our analysis on a data set of mobility traces of 37 users collected over a period of 1.5 years. Our results show that even algorithms achieving an overall high accuracy are unable to reliably predict the next location of the user if this is different from the current one. Building upon our analysis we then present a novel next-place prediction algorithm that can both achieve high overall accuracy and reliably predict transitions. Our approach combines all the 18 algorithms considered in our analysis and achieves its good performance at the cost of a higher computational and memory overhead.

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Cited By

Corrias RGjoreski MLangheinrich M(2023)Exploring Transformer and Graph Convolutional Networks for Human Mobility ModelingSensors10.3390/s2310480323:10(4803)Online publication date: 16-May-2023
https://doi.org/10.3390/s23104803
Luo YDuan HLiu YChung FFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Timestamps as Prompts for Geography-Aware Location RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615083(1697-1706)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615083
Ezequiel CGjoreski MLangheinrich M(2022)Federated Learning for Privacy-Aware Human Mobility ModelingFrontiers in Artificial Intelligence10.3389/frai.2022.8670465Online publication date: 28-Jun-2022
https://doi.org/10.3389/frai.2022.867046
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Index Terms

The influence of temporal and spatial features on the performance of next-place prediction algorithms
1. Information systems

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Published In

cover image ACM Conferences

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

September 2013

846 pages

ISBN:9781450317702

DOI:10.1145/2493432

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Copyright © 2013 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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 September 2013

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UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;

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

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UbiComp '24

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

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Cited By

Corrias RGjoreski MLangheinrich M(2023)Exploring Transformer and Graph Convolutional Networks for Human Mobility ModelingSensors10.3390/s2310480323:10(4803)Online publication date: 16-May-2023
https://doi.org/10.3390/s23104803
Luo YDuan HLiu YChung FFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Timestamps as Prompts for Geography-Aware Location RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615083(1697-1706)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615083
Ezequiel CGjoreski MLangheinrich M(2022)Federated Learning for Privacy-Aware Human Mobility ModelingFrontiers in Artificial Intelligence10.3389/frai.2022.8670465Online publication date: 28-Jun-2022
https://doi.org/10.3389/frai.2022.867046
Gjoreski MLaporte MLangheinrich M(2022)Toward privacy-aware federated analytics of cohorts for smart mobilityFrontiers in Computer Science10.3389/fcomp.2022.8912064Online publication date: 27-Jul-2022
https://doi.org/10.3389/fcomp.2022.891206
Huang ZMa JDong YFoutz NLi JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Empowering Next POI Recommendation with Multi-Relational ModelingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531801(2034-2038)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531801
Henouda SLaallam FKazar OSassi AAouarib HHoufani D(2022)WP-CamemBERT for next location Prediction2022 International Symposium on iNnovative Informatics of Biskra (ISNIB)10.1109/ISNIB57382.2022.10075929(1-6)Online publication date: 7-Dec-2022
https://doi.org/10.1109/ISNIB57382.2022.10075929
Li QZou DXu Y(2021)Combining individual travel behaviour and collective preferences for next location predictionTransportmetrica A: Transport Science10.1080/23249935.2021.196806618:3(1754-1776)Online publication date: 12-Sep-2021
https://doi.org/10.1080/23249935.2021.1968066
Trnka MSvacina JCerny TSong EHong JBures M(2020)Securing Internet of Things Devices Using The Network ContextIEEE Transactions on Industrial Informatics10.1109/TII.2019.295410016:6(4017-4027)Online publication date: Jun-2020
https://doi.org/10.1109/TII.2019.2954100
Yu ZWang ZYu ZWang Z(2020)Individual Behavior RecognitionHuman Behavior Analysis: Sensing and Understanding10.1007/978-981-15-2109-6_5(37-137)Online publication date: 1-Mar-2020
https://doi.org/10.1007/978-981-15-2109-6_5
Meurisch CGogel ABayrak BMühlhäuser MPoor VHan Z(2019)NextActProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360821(278-287)Online publication date: 12-Nov-2019
https://dl.acm.org/doi/10.1145/3360774.3360821
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