Exploring Georeferenced Mobile Phone Datasets – A Survey and Reference Framework

Yuan, Yihong; Raubal, Martin

doi:10.1111/gec3.12269

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…In the past, the elicitation of these data was based on paper or telephone surveys, but over the past decade the diversity of tracking-data sources multiplied and today, a manifold of different types of tracking data are available. Examples are global navigation satellite system (GNSS) tracking data (Zheng et al 2008), location data based on the proximity to WiFi hotspots (Sapiezynski et al 2015), location data from social networks (Hasan et al 2013), public transport smart card data (Zhong et al 2016), call detail record (CDR) data (González et al 2008;Yuan and Raubal 2016b;Yuan et al 2012), and credit-card transactions (Clemente et al 2018).…”

Section: Datamentioning

confidence: 99%

Geosmartness for Personalized and Sustainable Future Urban Mobility

2021

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Urban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

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Section: Datamentioning

confidence: 99%

Geosmartness for Personalized and Sustainable Future Urban Mobility

2021

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“…Sklearn module encapsulates commonly used machine learning algorithms, including regression, dimensionality reduction, classification, clustering. It is a simple and efficient data mining and data analysis tool [38]. To ensure the reliability and generalization performance of the model results, this paper randomly divides the samples in the ratio of 3:1.…”

Section: Model Accuracy Analysismentioning

confidence: 99%

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

Zhang

Xia

et al. 2019

Sustainability

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Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents' travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.

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“…Overall, these ubiquitous data are often used for location analytics to characterize various aspects of human mobility [10]. The data types can vary widely and be acquired by several means, including via Global System for Mobiles (GSM), Wi-Fi, Bluetooth, or Global Positioning System (GPS) [11,12]. In particular, Call Detail Records (CDRs) are a type of GSM data often collected by mobile operators and used to study human movements and social networks.…”

Section: Introductionmentioning

confidence: 99%

Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19

Rodrigues,

Veloso,

Alves

et al. 2023

IJGI

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The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population’s mobility and routine locations after the pandemic’s peak. We use clustering algorithms to identify citizens’ stops and routine locations, at an antenna level, during the following months after the pandemic’s first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ.

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Exploring Georeferenced Mobile Phone Datasets – A Survey and Reference Framework

Cited by 5 publications

References 44 publications

Geosmartness for Personalized and Sustainable Future Urban Mobility

Geosmartness for Personalized and Sustainable Future Urban Mobility

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19

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