Application of Call Detail Records - Chances and Obstacles

Mörner, Moritz von

doi:10.1016/j.trpro.2017.05.429

Cited by 28 publications

(16 citation statements)

References 5 publications

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“…Big data generation will vary geographically, and may be reduced in many high mobility contexts where infrastructure (i.e., cell towers, wi-fi connection and electronic bank transfer services) is less established. In addition, there are significant issues around the potential extraction of sensitive information contained in big data (23,24) and data sources are often fragmentated across disciplines which reduces the awareness of available datasets (25). Accounting for multiple biases and the complex analyses required to interpret the data are further examples of methodological difficulties associated with the use of big data (6,26).…”

Section: Accessing and Utilising Big Datamentioning

confidence: 99%

Key opportunities and challenges for the use of big data in migration research and policy

Franklinos

Parrish

Burns

et al. 2020

Preprint

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Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, has been proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the UN, humanitarian NGOs, policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns; and addressing political narratives. We advocate the need for increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.

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Section: Accessing and Utilising Big Datamentioning

confidence: 99%

Key opportunities and challenges for the use of big data in migration research and policy

Franklinos

Parrish

Burns

et al. 2020

Preprint

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“…Several metastudies list a large number of case-studies with focus on travel behaviour [5], specific methods like machine learning [9], and cellular network data in context of other new data sources like global positioning system (GPS) tracks and smartcard data from public transit systems [4]. A summary of the potential and obstacles of using cellular network data for traffic analysis has been given by von Mörner [10].…”

Section: Previous Researchmentioning

confidence: 99%

Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

Breyer

Rydergren

Gundlegård

2020

Journal of Advanced Transportation

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Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods.

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“…• On the one hand, these solutions basically rely on the raw spatio-temporal trajectories generated by different moving objects like taxis or individuals reporting their current location every few seconds or minutes. Nonetheless, in a real-world scenario this type of high-quality location data is, in most occasions, rather inaccessible due to several privacy and economic policies defined by data providers and operators [9]. At the same time, the open data movement has promoted the release of an increasing number of human-mobility datasets [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Human Mobility Prediction With Region-Based Flows and Water Consumption

et al. 2021

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We are witnessing an increasing need to accurately measure people's mobility as it has become an instrumental factor for the development of innovative services in multiple domains. In this context, several ICT solutions have relied on location-based technologies such as GPS, WiFi or Bluetooth to track individuals movements. However, these technologies are limited by the privacy restrictions of data providers. In this paper we propose a methodology to robustly predict citizens' mobility patterns based on heterogeneous data from different sources. Particularly, our methodology focuses on a human mobility predictor based on a low-resolution mobility dataset and the use of water consumption data as a facilitator of this prediction task. As a result, this work explores whether the water consumption within a geographical region can reveal human activity patterns relevant from the point of view of the mobility mining discipline. This approach has been tested in a residential area near Madrid (Spain) obtaining quite promising results.

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Application of Call Detail Records - Chances and Obstacles

Cited by 28 publications

References 5 publications

Key opportunities and challenges for the use of big data in migration research and policy

Key opportunities and challenges for the use of big data in migration research and policy

Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

Human Mobility Prediction With Region-Based Flows and Water Consumption

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