Advances by using Mobile Phone Data in mobility analysis in the Netherlands

Friso, K.; Oakil, Abu Toasin

doi:10.1109/mtits.2019.8883346

Cited by 1 publication

(1 citation statement)

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“…Mobile phone data is a hot topic in the field of human mobility studies. A vast amount of recent research has been performed that investigates the use of mobile phone data to gather better insights into behaviour, social networks and mobility patterns of the masses [3][4][5][6][7][8]. Mobile phone data is regarded a prime candidate to replace traditional mobility measurement techniques such as surveys and roadside measurements [9,10].…”

Section: Introductionmentioning

confidence: 99%

Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities

et al. 2020

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Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies—for one, during weekends—calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information.

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