On estimating the predictability of human mobility: the role of routine
Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expresse...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Teixeira, Douglas do Couto [verfasserIn] Almeida, Jussara M. [verfasserIn] Viana, Aline Carneiro [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: EPJ Data Science - Berlin : SpringerOpen, 2012, 10(2021), 1 vom: 29. Sept. |
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| Übergeordnetes Werk: |
volume:10 ; year:2021 ; number:1 ; day:29 ; month:09 |
| Links: |
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| DOI / URN: |
10.1140/epjds/s13688-021-00304-8 |
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SPR045186855 |
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| 520 | |a Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. | ||
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10.1140/epjds/s13688-021-00304-8 doi (DE-627)SPR045186855 (SPR)s13688-021-00304-8-e DE-627 ger DE-627 rakwb eng 540 ASE Teixeira, Douglas do Couto verfasserin aut On estimating the predictability of human mobility: the role of routine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. Human mobility (dpeaa)DE-He213 Predictability (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Mobility metrics (dpeaa)DE-He213 Almeida, Jussara M. verfasserin aut Viana, Aline Carneiro verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 10(2021), 1 vom: 29. Sept. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:10 year:2021 number:1 day:29 month:09 https://dx.doi.org/10.1140/epjds/s13688-021-00304-8 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 10 2021 1 29 09 |
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10.1140/epjds/s13688-021-00304-8 doi (DE-627)SPR045186855 (SPR)s13688-021-00304-8-e DE-627 ger DE-627 rakwb eng 540 ASE Teixeira, Douglas do Couto verfasserin aut On estimating the predictability of human mobility: the role of routine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. Human mobility (dpeaa)DE-He213 Predictability (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Mobility metrics (dpeaa)DE-He213 Almeida, Jussara M. verfasserin aut Viana, Aline Carneiro verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 10(2021), 1 vom: 29. Sept. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:10 year:2021 number:1 day:29 month:09 https://dx.doi.org/10.1140/epjds/s13688-021-00304-8 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 10 2021 1 29 09 |
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10.1140/epjds/s13688-021-00304-8 doi (DE-627)SPR045186855 (SPR)s13688-021-00304-8-e DE-627 ger DE-627 rakwb eng 540 ASE Teixeira, Douglas do Couto verfasserin aut On estimating the predictability of human mobility: the role of routine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. Human mobility (dpeaa)DE-He213 Predictability (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Mobility metrics (dpeaa)DE-He213 Almeida, Jussara M. verfasserin aut Viana, Aline Carneiro verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 10(2021), 1 vom: 29. Sept. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:10 year:2021 number:1 day:29 month:09 https://dx.doi.org/10.1140/epjds/s13688-021-00304-8 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 10 2021 1 29 09 |
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10.1140/epjds/s13688-021-00304-8 doi (DE-627)SPR045186855 (SPR)s13688-021-00304-8-e DE-627 ger DE-627 rakwb eng 540 ASE Teixeira, Douglas do Couto verfasserin aut On estimating the predictability of human mobility: the role of routine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. Human mobility (dpeaa)DE-He213 Predictability (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Mobility metrics (dpeaa)DE-He213 Almeida, Jussara M. verfasserin aut Viana, Aline Carneiro verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 10(2021), 1 vom: 29. Sept. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:10 year:2021 number:1 day:29 month:09 https://dx.doi.org/10.1140/epjds/s13688-021-00304-8 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 10 2021 1 29 09 |
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10.1140/epjds/s13688-021-00304-8 doi (DE-627)SPR045186855 (SPR)s13688-021-00304-8-e DE-627 ger DE-627 rakwb eng 540 ASE Teixeira, Douglas do Couto verfasserin aut On estimating the predictability of human mobility: the role of routine 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. Human mobility (dpeaa)DE-He213 Predictability (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Mobility metrics (dpeaa)DE-He213 Almeida, Jussara M. verfasserin aut Viana, Aline Carneiro verfasserin aut Enthalten in EPJ Data Science Berlin : SpringerOpen, 2012 10(2021), 1 vom: 29. Sept. (DE-627)737702664 (DE-600)2705691-0 2193-1127 nnns volume:10 year:2021 number:1 day:29 month:09 https://dx.doi.org/10.1140/epjds/s13688-021-00304-8 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 10 2021 1 29 09 |
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On estimating the predictability of human mobility: the role of routine |
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Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. © The Author(s) 2021 |
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Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. © The Author(s) 2021 |
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Abstract Given the difficulties in predicting human behavior, one may wish to establish bounds on our ability to accurately perform such predictions. In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations. © The Author(s) 2021 |
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In the case of mobility-related behavior, there exists a fundamental technique to estimate the predictability of an individual’s mobility, as expressed in a given dataset. Although useful in several scenarios, this technique focused on human mobility as a monolithic entity, which poses challenges to understanding different types of behavior that may be hard to predict. In this paper, we propose to study predictability in terms of two components of human mobility: routine and novelty, where routine is related to preferential returns, and novelty is related to exploration. Viewing one’s mobility in terms of these two components allows us to identify important patterns about the predictability of one’s mobility. Additionally, we argue that mobility behavior in the novelty component is hard to predict if we rely on the history of visited locations (as the predictability technique does), and therefore we here focus on analyzing what affects the predictability of one’s routine. To that end, we propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one’s routine deviates from a reference routine that is completely predictable, therefore estimating the amount of hard-to-predict behavior in one’s routine. Finally, we rely on previously proposed metrics, as well as a newly proposed one, to understand what affects the predictability of a person’s routine. Our experiments show that our metrics are able to capture most of the variability in one’s routine (adjusted $R^{2}$ of up to 84.9% and 96.0% on a GPS and CDR datasets, respectively), and that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human mobility</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Predictability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Entropy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobility metrics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Almeida, Jussara M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Viana, Aline Carneiro</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EPJ Data Science</subfield><subfield code="d">Berlin : SpringerOpen, 2012</subfield><subfield code="g">10(2021), 1 vom: 29. 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