An Analysis of Human Mobility Using Real Traces

Azevedo, Tiago; Bezerra, Rafael L.; Campos, Carlos Alberto Vieira; Moraes, Luís F. M. de

doi:10.1109/wcnc.2009.4917569

Cited by 31 publications

(22 citation statements)

References 15 publications

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“…With the extensive records of mobile phone data such as calling pattern and location of the mobile phone user, analyses have been performed on numerous aspects including behavioral routine [7][8] [9], social proximity [10][11], call prediction [12] [13], social closeness [14] [15], and human mobility [16][17] [18][19] [20].…”

Section: Related Workmentioning

confidence: 99%

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Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

Phithakkitnukoon

Horanont

Lorenzo

et al. 2010

Lecture Notes in Computer Science

218

137

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Abstract. Being able to understand dynamics of human mobility is essential for urban planning and transportation management. Besides geographic space, in this paper, we characterize mobility in a profilebased space (activity-aware map) that describes most probable activity associated with a specific area of space. This, in turn, allows us to capture the individual daily activity pattern and analyze the correlations among different people's work area's profile. Based on a large mobile phone data of nearly one million records of the users in the central Metro-Boston area, we find a strong correlation in daily activity patterns within the group of people who share a common work area's profile. In addition, within the group itself, the similarity in activity patterns decreases as their work places become apart.

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Section: Related Workmentioning

confidence: 99%

“…To study dynamics in human mobility, GPS receiver has been handy for researchers in collecting a large real-life traces. Azevedo et al [16] study pedestrian mobility behavior using GPS traces captured at Quinta da Boa Vista's Park in Rio de Janeiro (Brazil). Movement elements are analyzed from data collected from 120 pedestrians.…”

Section: Related Workmentioning

confidence: 99%

Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

Phithakkitnukoon

Horanont

Lorenzo

et al. 2010

Lecture Notes in Computer Science

218

137

View full text Add to dashboard Cite

show abstract

“…The second question relates to the personal duration at each purposive location, and it has also been investigated in other literature with different contexts [7,15,16]. This measurement has a great influence on the diffusion [6,7] of human mobility, and hence it plays an important role in infectious disease control and urban planning.…”

Section: Characteristics Of Human Mobilitymentioning

confidence: 99%

“…Azevedo et al [15] analyzed, at the city level, several motion components of pedestrian movement in real scenarios and reported that the pause time followed a log-normal distribution. Analyzed by the AIC-based model selection method, the displacements of taxi trajectories were reported to follow an exponential distribution [16].…”

Section: Introductionmentioning

confidence: 99%

An empirical study on human mobility and its agent-based modeling

Jia¹,

Jiang²,

Carling³

et al. 2012

J. Stat. Mech.

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Abstract. This paper aims to analyze the GPS traces of 258 volunteers in orderto obtain a better understanding of both the human mobility patterns and the mechanism. We report the regular and scaling properties of human mobility for several aspects, and importantly we identify its Levy flight characteristic, which is consistent with those from previous studies. We further assume two factors that may govern the Levy flight property: (1) the scaling and hierarchical properties of the purpose clusters which serve as the underlying spatial structure, and (2) the individual preferential behaviors. To verify the assumptions, we implement an agent-based model with the two factors, and the simulated results do indeed capture the same Levy flight pattern as is observed. In order to enable the model to reproduce more mobility patterns, we add to the model a third factor: the jumping factor, which is the probability that one person may cancel their regular mobility schedule and explore a random place. With this factor, our model can cover a relatively wide range of human mobility patterns with scaling exponent values from 1.55 to 2.05.

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“…Recently, with the large datasets made available by academic institutions ( [37] [41] [13]) and industries [3] [45], researchers are given the unique opportunity to analyze human mobility at a finer gained level [4] [28] [9] [16] [46]. With the rapidly increase of mobile phone users, mobile phones become the sensors of the city -sensing social networks [38] [39] [16] [13], social events [8], and activities [40].…”

Section: Introductionmentioning

confidence: 99%

Inferring Asymmetry of Inhabitant Flow using Call Detail Records

Phithakkitnukoon¹,

Ratti²

2011

JAIT

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Abstract-In this research, we carry out a study of the inhabitant flow using a large mobile phone data with location estimates from subscribers in Suffolk county, Massachusetts, USA that reveals the asymmetry in the flows, which reflects the way that people travel daily. People occasionally travel in a non-symmetrical way. For instance, they would take one route traveling from home to a destination and a different route while returning home. By analyzing the flow over the space, the results show that there exists asymmetrical flows, which account for 33% of all inhabitant flows. In addition, high asymmetrical flows are observed in trips between low and high congested areas e.g. urban and suburban areas, as well as trips made to and from low populated areas e.g. commercial areas.

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An Analysis of Human Mobility Using Real Traces

Cited by 31 publications

References 15 publications

Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

An empirical study on human mobility and its agent-based modeling

Inferring Asymmetry of Inhabitant Flow using Call Detail Records

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