A review of urban computing for mobile phone traces

Abstract

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.

Urban Sensing Using Mobile Phone Network Data: A Survey of Research

“…The work in Krisp [2010] shows how calculating and visualizing mobile phone density can assist fire and rescue services. Moreover, in , the information derived from the aggregated use of cell phone records is used to identify the socioeconomic levels of a population.…”

“…For example, fixed both a spatial S th and a temporal T th threshold in order to detect stops; that is, two consecutive stops stop i and stop j can be collapsed in the same stop if distance(d stop i , d stop i ) < S th and (t stop i − t stop j ) > T th . A similar approach was also used in Jiang et al [2013].…”

Collective benefits in traffic during mega events via the use of information technologies

“…Technological innovations, socio-demographic shifts and political decisions shape the way people move in cities [1][2][3][4][5][6]. The amount of time invested to move to work every day [7][8][9][10] has important implications in the well functioning of our cities. They affect total energy use, equity, air pollution, and urban sprawling.…”

Análise de deslocamentos dos moradores de assentamentos precários a partir de dados da telefonia móvel em São Paulo.

“…Cada CDR contém um identificador anônimo do usuário, longitude, latitude e tempo em cada instância das ligações ou outro uso do celular (Alexander et al, 2015). Como cada CDR possui informações de data e hora do uso, foi inserido um campo com marcação de uma em uma hora para futuro agrupamento de tempo, como realizado em Jiang et al (2013). Outro procedimento feito foi identificar o dia da semana do uso (segunda, terça, quarta, quinta, sexta, sábado ou domingo) através da data, para ajudar em futuros filtros da análise espaço-temporal.…”

“…Para fazer a correlação desses pontos no espaço foi utilizada a marcação por célula (Jiang et al, 2013) Em cada célula foi registrado o percentual de uso do solo correspondente. As células podem abranger mais de um tipo de solo por isso foi feita a normalização dos valores para fazer a inferência aplicando a seguinte a equação 1.…”