Understanding individual mobility patterns from urban sensing data: A mobile phone trace example

Calabrese, Francesco; Diao, Mi; Lorenzo, Giusy Di; Ferreira, Joseph; Ratti, Carlo

doi:10.1016/j.trc.2012.09.009

Cited by 507 publications

(362 citation statements)

References 23 publications

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“…[10] works on tracing human mob ility using mobile phone data, in spite of highly utilization of this data in mult i fields, but this research focuses on its usage in planning to transportation system, it has comparing between two data sources the mobile data and odometer reading fro m safety inspections of vehicles at the same region. It co mpares mobility features of mobile phone traces with mobility features of odometer readings from annual safety inspections of all private vehicles registered in the Boston Metropolitan Area.…”

Section: The Case Study Of Cdrsmentioning

confidence: 99%

Adaptive Modeling of Urban Dynamics during Armada Event using CDRs

Behadili¹,

Bertelle²,

George

2017

IJITCS

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Abstract-This study investigates the mobile phone data during ephemeral event (Armada). The statistical techniques have been used for modeling hu man mobility collectively and individually. The undertaken substantial parameters are: inter-event times, t ravel d istances (displacements), and radius of gyration. They have been analyzed and simu lated using computing plat form by integrating various applications for huge database management, visualizat ion, analysis, and simulation. Accordingly, the general population pattern law has been extracted. This study has revealed the individuals mobility in dynamic perspective for 615,712 mob ile users, also the simulated observed data are classified according to general, work, and off days.

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Section: The Case Study Of Cdrsmentioning

confidence: 99%

Adaptive Modeling of Urban Dynamics during Armada Event using CDRs

Behadili¹,

Bertelle²,

George

2017

IJITCS

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“…In Figure 1 we report a small sample for each kind of recorded activity accompanied by a mobility trace that comes from combining the CDR entries. One of the advantages of this dataset with respect to other datasets [17,10,19,3,12] is the chance to leverage the Internet access data for purposes of mobility pattern analysis [4]. Although CDRs are rich sources for studying and analyzing human activities in different fields, they have two significant drawbacks as to providing location information.…”

Section: Call Detail Records Datasetsmentioning

confidence: 99%

“…The proposed classification and the PoIs and user features provide the basis for understanding human behavior by extracting the semantics of visited places. In line with similar works [10,23,15,33], we used a heuristic approach for the semantic analysis and experimented it on a large dataset containing mobility patterns of hundred thousands of people in a metropolitan area.…”

Section: Introductionmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.

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“…They can also be used alone, from the communication infrastructure side, such as cell tower traces or WiFi access points traces (Bekhor et al;Calabrese et al;, or from the individuals' devices (Etter et al;Buisson;Chen and Yang;Carrel et al;. Etter et al (2012) show that it is possible to predict up to 60% of next visited places from passive smartphone data.…”

Section: From Diary Surveys To Location-aware Technologiesmentioning

confidence: 99%

Location choice with longitudinal WiFi data

Danalet

Tinguely

Lapparent

et al. 2016

Journal of Choice Modelling

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While moving from diary survey to location-aware technologies, recent data collection techniques provide new insights about location choices. Only few dynamic models of location choice exist in the literature, and none of them to our knowledge correct for serial correlation. In this paper, we apply a method proposed by Wooldridge (2005) to deal with the initial values problem on the choice of catering locations on a campus using WiFi traces. Cross-validation, price elasticity and simulation of a scenario predicting the opening of a new catering location are presented. Predicted market shares of the new catering location correspond to point-of-sale data of the first week of opening.

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Understanding individual mobility patterns from urban sensing data: A mobile phone trace example

Cited by 507 publications

References 23 publications

Adaptive Modeling of Urban Dynamics during Armada Event using CDRs

Adaptive Modeling of Urban Dynamics during Armada Event using CDRs

On the properties of human mobility

Location choice with longitudinal WiFi data

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