AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data

Berlingerio, Michele; Calabrese, Francesco; Lorenzo, Giusy Di; Nair, Rahul; Pinelli, Fabio; Sbodio, Marco Luca

doi:10.1007/978-3-642-40994-3_50

Cited by 90 publications

(79 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We use the speedup metric with different numbers of computing cores (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) to evaluate the scalability of our distributed learning algorithm.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Dong et al [4] investigate the mobile call duration behaviors in mobile social networks and find that people who are familiar with each other tend to make short calls. Recently, the emergence of work on mobility [30,33,8,36] and location-based mobile networks [7,2,1], where human movements or locations are tracked by mobile phones, provides us a means of understanding and predicting mobile social behaviors. Eagle et al [6] try to infer the friendship network in mobile phone data.…”

Section: Related Workmentioning

confidence: 99%

“…For example, a postpaid mobile user 1 is required to create an account by providing detailed demographic information (e.g., name, age, gender, etc.). However, a recent report 2 indicates that there is still a large portion of prepaid users 3 (also commonly referred to as pay-as-you-go) who are required to purchase credit in advance of service use. Statistics show that 95% of mobile users in India are prepaid, 80% in Latin America, 70% in China, 65% in Europe, and 33% in the United States.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inferring user demographics and social strategies in mobile social networks

Dong

Yang

Tang

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

167

102

View full text Add to dashboard Cite

Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns.Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable predictioninferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.

show abstract

Section: Experiments Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inferring user demographics and social strategies in mobile social networks

Dong

Yang

Tang

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

167

102

View full text Add to dashboard Cite

show abstract

“…Systems for mining big data from mobile devices may play a particularly important role in developing countries, where no governmental and health infrastructures are present. In these countries, systems based on mobile phone data analysis can be used to improve basic services, such as mass transit planning [36] and disease prevention [37].…”

Section: Trendsmentioning

confidence: 99%

Software engineering for mobility: reflecting on the past, peering into the future

Picco

Julien

Murphy

et al. 2014

Future of Software Engineering Proceedings

View full text Add to dashboard Cite

At the end of the second millennium, mobility was a hot research topic. Physical mobility of devices was becoming commonplace with the availability of cheap wireless cards, the first attempts to transform phones into personal do-it-all devices were beginning to appear, and mobile ad hoc networks were attracting a huge interest from many research communities. Logical mobility of code was still going strong as a design option for distributed systems, with the Java language providing some of the ready-to-use building blocks. In 2000, when we put forth a research "roadmap" for software engineering for mobility, the challenges posed by this dynamic scenario were many.A decade and a half later, many things have changed. Mobility is no longer exotic: we juggle multiple personal devices every day while on the move, plus we grab and update applications on a whim from virtual stores. Indeed, some trends and visions we considered in our original paper materialized, while others faded, disappeared, or morphed into something else. Moreover, some players unexpected at the time (e.g., cloud computing and online social networks) appeared on the scene as game changers.In this paper we revisit critically our original vision, reflecting on the past and peering into the future of the lively and exciting research area of mobility. Further, we ask ourselves to what extent the software engineering community is still interested in taking up the challenges mobility bears.

show abstract

“…CDRs can also answer more mundane questions. In 2012, the phone company Orange made 5 mo of Cote d'Ivoire mobile phone metadata available as part of a research challenge (7); researchers showed that calling patterns even help in planning bus routes (8).…”

mentioning

confidence: 99%

Transactional information is remarkably revelatory

Landau

2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data

Cited by 90 publications

References 4 publications

Inferring user demographics and social strategies in mobile social networks

Inferring user demographics and social strategies in mobile social networks

Software engineering for mobility: reflecting on the past, peering into the future

Transactional information is remarkably revelatory

Contact Info

Product

Resources

About