Analyzing large-scale human mobility data: a survey of machine learning methods and applications

Toch, Eran; Lerner, Boaz; Ben‐Zion, Eyal; Ben‐Gal, Irad

doi:10.1007/s10115-018-1186-x

Cited by 132 publications

(68 citation statements)

References 84 publications

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“…Therefore for effective results LBS should provide information not only based on location but also focus on context information of the requester [2]. Another example is location based advertising that provides richer and more meaningful information based on the user context [3]. The user might be happy to receive a surprise discount offer from his/her favorite restaurant during an evening drive.…”

Section: Context Aware Location Information Services (2006 -2010)mentioning

confidence: 99%

Examination of Location Based Services and User Preferences

2019

IJITEE

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Recent developments on mobile location information have driven efforts to mine user patterns of interest. Even start-up companies survey user interests to enrich their business. All medium and large organizations are paying attention to collect and store location data. With the support of unlimited computing power and memory of mobile phones we can apply proficient Deep Learning algorithms to determine an optimal solution for user interests. In this article, we aim to complete an overall survey on evolution of Location Based Services and the improvements in recent trends. We have categorized the evolution period in to three divisions covering from the year 2000 to till date.

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Section: Context Aware Location Information Services (2006 -2010)mentioning

confidence: 99%

Examination of Location Based Services and User Preferences

2019

IJITEE

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“…Our daily lives are shaped by the convolution of a broad range of individual and social-level demands (e.g., eat, sleep, work, pay bills), and mobility is essential for their fulfilment. Hence, the betterment of our lives passes through the study of how people move [2,17]. Some models on mobility assume that all travellers are -more or less -the same, disregarding the wealth of attributes discriminating one social group from another.…”

Section: Introductionmentioning

confidence: 99%

Gender Patterns of Human Mobility in Colombia: Reexamining Ravenstein’s Laws of Migration

Macedo¹,

Lotero²,

Cardillo³

et al. 2020

Complex Networks XI

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0000−0002−7071−379X] , Laura Lotero 2[0000−0002−6537−3276] , Alessio Cardillo 3,4,5[0000−0003−4811−9978] , Hugo Barbosa 1[0000−0002−3927−969X] , and Ronaldo Menezes 1[0000−0002−6479−6429]Abstract. Public stakeholders implement several policies and regulations to tackle gender gaps, fostering the change in the cultural constructs associated with gender. One way to quantify if such changes elicit gender equality is by studying mobility. In this work, we study the daily mobility patterns of women and men occurring in Medellín (Colombia) in two years: 2005 and 2017. Specifically, we focus on the spatiotemporal differences in the travels and find that purpose of travel and occupation characterise each gender differently. We show that women tend to make shorter trips, corroborating Ravenstein's Laws of Migration. Our results indicate that urban mobility in Colombia seems to behave in agreement with the "archetypal" case studied by Ravenstein.

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“…This data provided information based on many people, but with locational and temporal accuracy issues related to connectivity to the cellphone tower network and peoples' active use of the phone [22]. With the addition of GPS instrumentation and the growing ubiquity of smartphone-style cellular telephones, these digital traces have become more accurate, more frequent and represent an increasing proportion of the population [23][24][25][26][27][28]. The devicelevel, raw data are collected by cellular telephone providers, GPS enabled devices and increasingly by smartphone applications.…”

Section: Introductionmentioning

confidence: 99%

“…The devicelevel, raw data are collected by cellular telephone providers, GPS enabled devices and increasingly by smartphone applications. Several third-party providers on the commercial market combine this information and sell processed data in a variety of formats [28].…”

Section: Introductionmentioning

confidence: 99%

Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA.

Merrill¹,

Atkinson²,

Mulvaney³

et al. 2019

Preprint

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We introduce and validate the use of commercially-available datasets of human mobility based on cell phone locational data to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to over 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. The high-resolution information in both space and time provided by cell phone location derived data creates opportunities for developing next-generation models of human interactions with the natural environment. However, the opaque and rapidly developing methods for processing locational information by the data providers required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation.

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Analyzing large-scale human mobility data: a survey of machine learning methods and applications

Cited by 132 publications

References 84 publications

Examination of Location Based Services and User Preferences

Examination of Location Based Services and User Preferences

Gender Patterns of Human Mobility in Colombia: Reexamining Ravenstein’s Laws of Migration

Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA.

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