Understanding predictability and exploration in human mobility

Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which und...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Cuttone, Andrea [verfasserIn] Lehmann, Sune [verfasserIn] González, Marta C. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	human mobility next-location prediction predictability

Übergeordnetes Werk:	Enthalten in: EPJ Data Science - Berlin : SpringerOpen, 2012, 7(2018), 1 vom: 11. Jan.
Übergeordnetes Werk:	volume:7 ; year:2018 ; number:1 ; day:11 ; month:01

Links:	Volltext

DOI / URN:	10.1140/epjds/s13688-017-0129-1

Katalog-ID:	SPR032168365

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520			\|a Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
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spelling	10.1140/epjds/s13688-017-0129-1 doi (DE-627)SPR032168365 (SPR)s13688-017-0129-1-e DE-627 ger DE-627 rakwb eng 540 ASE Cuttone, Andrea verfasserin aut Understanding predictability and exploration in human mobility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility. human mobility (dpeaa)DE-He213 next-location prediction (dpeaa)DE-He213 predictability (dpeaa)DE-He213 Lehmann, Sune verfasserin aut González, Marta C. verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 7(2018), 1 vom: 11. Jan. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:7 year:2018 number:1 day:11 month:01 https://dx.doi.org/10.1140/epjds/s13688-017-0129-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018 1 11 01
allfields_unstemmed	10.1140/epjds/s13688-017-0129-1 doi (DE-627)SPR032168365 (SPR)s13688-017-0129-1-e DE-627 ger DE-627 rakwb eng 540 ASE Cuttone, Andrea verfasserin aut Understanding predictability and exploration in human mobility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility. human mobility (dpeaa)DE-He213 next-location prediction (dpeaa)DE-He213 predictability (dpeaa)DE-He213 Lehmann, Sune verfasserin aut González, Marta C. verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 7(2018), 1 vom: 11. Jan. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:7 year:2018 number:1 day:11 month:01 https://dx.doi.org/10.1140/epjds/s13688-017-0129-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018 1 11 01
allfieldsGer	10.1140/epjds/s13688-017-0129-1 doi (DE-627)SPR032168365 (SPR)s13688-017-0129-1-e DE-627 ger DE-627 rakwb eng 540 ASE Cuttone, Andrea verfasserin aut Understanding predictability and exploration in human mobility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility. human mobility (dpeaa)DE-He213 next-location prediction (dpeaa)DE-He213 predictability (dpeaa)DE-He213 Lehmann, Sune verfasserin aut González, Marta C. verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 7(2018), 1 vom: 11. Jan. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:7 year:2018 number:1 day:11 month:01 https://dx.doi.org/10.1140/epjds/s13688-017-0129-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018 1 11 01
allfieldsSound	10.1140/epjds/s13688-017-0129-1 doi (DE-627)SPR032168365 (SPR)s13688-017-0129-1-e DE-627 ger DE-627 rakwb eng 540 ASE Cuttone, Andrea verfasserin aut Understanding predictability and exploration in human mobility 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility. human mobility (dpeaa)DE-He213 next-location prediction (dpeaa)DE-He213 predictability (dpeaa)DE-He213 Lehmann, Sune verfasserin aut González, Marta C. verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 7(2018), 1 vom: 11. Jan. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:7 year:2018 number:1 day:11 month:01 https://dx.doi.org/10.1140/epjds/s13688-017-0129-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018 1 11 01
language	English
source	Enthalten in EPJ Data Science 7(2018), 1 vom: 11. Jan. volume:7 year:2018 number:1 day:11 month:01
sourceStr	Enthalten in EPJ Data Science 7(2018), 1 vom: 11. Jan. volume:7 year:2018 number:1 day:11 month:01
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topic_facet	human mobility next-location prediction predictability
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container_title	EPJ Data Science
authorswithroles_txt_mv	Cuttone, Andrea @@aut@@ Lehmann, Sune @@aut@@ González, Marta C. @@aut@@
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author	Cuttone, Andrea
spellingShingle	Cuttone, Andrea ddc 540 misc human mobility misc next-location prediction misc predictability Understanding predictability and exploration in human mobility
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topic_title	540 ASE Understanding predictability and exploration in human mobility human mobility (dpeaa)DE-He213 next-location prediction (dpeaa)DE-He213 predictability (dpeaa)DE-He213
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title	Understanding predictability and exploration in human mobility
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title_full	Understanding predictability and exploration in human mobility
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title_sort	understanding predictability and exploration in human mobility
title_auth	Understanding predictability and exploration in human mobility
abstract	Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
abstractGer	Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
abstract_unstemmed	Abstract Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
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title_short	Understanding predictability and exploration in human mobility
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