Modeling High Predictability and Scaling Laws of Human Mobility

Lin, Mingzhu; Hsu, Wen-Jing; Lee, Zhuo Qi

doi:10.1109/mdm.2013.81

“…Our paper is different from previous work about predictability [5,37] in two important aspects. First, the goal of our paper is different from that of those prior studies, where the authors investigated how the exploration (or novelty) part of a person's mobility trace impacts predictability.…”

Section: Related Workcontrasting

confidence: 62%

On estimating the predictability of human mobility: the role of routine

Teixeira

¹

,

Almeida

²

,

Viana

³

2021

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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 $R^{2}$ 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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“…In order to ascribe meaning to the torrents of data that are collected daily, it is necessary to go beyond pure empiricism by recognizing biases and testing the data against theory so that patterns within the data can be associated with processes (Kitchin 2014a). For example, there has been a wave of interest in human movement under the banner of 'human mobility', but this new thrust of research has moved away from trying to understand processes and tends to focus on predicting the movement of individuals (e.g., Lin et al 2013;Pirozmand et al 2014;Do and Gatica-Perez 2014) as opposed to aggregate flows or establishing regularities about the data (e.g., Brockmann et al 2006;Gonzalez et al 2008;Han et al 2009;Bazzani et al 2010;Liang et al 2012;Wang et al 2014). In the context of transportation modeling, it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes.…”

Section: A New Era Of Spatial Interactionmentioning

confidence: 99%

Potential and Pitfalls of Big Transport Data for Spatial Interaction Models of Urban Mobility

Oshan

¹

2020

The Professional Geographer

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Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset-the New York City (NYC) taxi trip data-is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.

show abstract

“…In order to ascribe meaning to the torrents of data that are collected daily, it is necessary to go beyond pure empiricism by recognizing biases and testing the data against theory so that patterns within the data can be associated with processes (Kitchin 2014a). For example, there has been a wave of interest in human movement under the banner of 'human mobility', but this new thrust of research has moved away from trying to understand processes and tends to focus on predicting the movement of individuals (e.g., Lin et al 2013;Pirozmand et al 2014;Do and Gatica-Perez 2014) as opposed to aggregate flows or establishing regularities about the data (e.g., Brockmann et al 2006;Gonzalez et al 2008;Han et al 2009;Bazzani et al 2010;Liang et al 2012;Wang et al 2014). In the context of transportation modeling, it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes.…”

Section: A New Era Of Spatial Interactionmentioning

confidence: 99%

Potential and pitfalls of big transport data for spatial interaction models of urban mobility

Oshan¹

2020

Preprint

0

View full text Add to dashboard Cite

Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset -the New York City (NYC) taxi trip data -is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.

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Modeling High Predictability and Scaling Laws of Human Mobility

Cited by 9 publications

References 14 publications

On estimating the predictability of human mobility: the role of routine

On estimating the predictability of human mobility: the role of routine

Potential and Pitfalls of Big Transport Data for Spatial Interaction Models of Urban Mobility

Potential and pitfalls of big transport data for spatial interaction models of urban mobility

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