Correlating mobile phone usage and travel behavior – A case study of Harbin, China

Yuan, Yihong; Raubal, Martin; Liu, Yu

doi:10.1016/j.compenvurbsys.2011.07.003

Cited by 212 publications

(115 citation statements)

References 18 publications

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“…(i.e. Liu et al (2013) and Yuan et al (2012)). The work proposed by Ahas et al (2010), for instance, employed mobile phone data from the city of Tallinn to classify travellers (i.e.…”

Section: Mobility Patterns With Intelligent Transport Systemsmentioning

confidence: 99%

Classification of Livebus arrivals user behavior

Hadjidimitriou

Mamei

Dell’Amico

et al. 2016

Journal of Intelligent Transportation Systems

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“…(i.e. Liu et al (2013) and Yuan et al (2012)). The work proposed by Ahas et al (2010), for instance, employed mobile phone data from the city of Tallinn to classify travellers (i.e.…”

Section: Mobility Patterns With Intelligent Transport Systemsmentioning

confidence: 99%

Classification of Livebus arrivals user behavior

Hadjidimitriou

Mamei

Dell’Amico

et al. 2016

Journal of Intelligent Transportation Systems

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“…These spatiotemporal data collection systems are useful for human mobility analysis, granting more efficiency and response capacity in urban policies. Moreover, contrary to other mobility data collection methods, geolocation is cheaper, provides samples with more information, updates more frequently, and has more spatial and temporal coverage, due to these reasons, a large number of researches have focused on the impact of mobile devices in human mobility patterns [41], [50], [52], [53], [54]. Table II summarizes some methods used for data analysis through geolocation mobile devices.…”

Section: Mobil Devices With Geolocationmentioning

confidence: 99%

“…Additionally, individual attributes are examined -age, gender, and income level, as well as supraindividual features like socio-institutional and physical aspects. The dataset provides three types of information directly registered by each mobile phone user: (1) mobile phone use; (2) individual features; (3) spatio-temporal points for a given time lapse [54]. The data analysis was carried out as follows: in first place, all the data was divided in groups in accordance with usage frequency -using dialed and received calls information, i.e., people belonging to the same group have the same call frequency.…”

Section: Correlating Mobile Phone Usage and Travel Behavior -A Case Smentioning

confidence: 99%

See 1 more Smart Citation

Urban Analysis and Smart Communities: An Approach to the Use of Technology in Everyday Mobility

Maury

Vilas

2015

Inge CUC

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Abstract-Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens' mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart community.Keywords--Smart Community, Urban Analysis, Pattern Mobility, Smart Cards, Geolocation, Geotagging.Resumen--La concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente.

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“…A large number of studies has focused on the recording and analysis of mobility and activity data using smartphones, usually by utilizing location (GPS) and accelerometer data ( [18], [19], [20], [21], [22], [23], [24], [25], [26]). Issues such as suitability of sensors for activity recognition [27], accuracy of transport mode classification [28], [29] and energy consumption of the app [30] are well researched areas.…”

Section: Large Scale Automatic Mobility Monitoringmentioning

confidence: 99%

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Bucher¹,

Cellina²,

Mangili³

et al. 2016

Proceedings of ICT for Sustainability 2016

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Abstract-The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.

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Correlating mobile phone usage and travel behavior – A case study of Harbin, China

Cited by 212 publications

References 18 publications

Classification of Livebus arrivals user behavior

Classification of Livebus arrivals user behavior

Urban Analysis and Smart Communities: An Approach to the Use of Technology in Everyday Mobility

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

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