Abstract
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.
Highlights
Samples are strongly influenced by the information present in the environment [1]
The bias we identified is introduced by demographic discrepancies between the traveling population and the sample from which mobile phone data is created
The main goal of this study is to provide a validated scaling method for mobile phone data that could be used in future studies to get an unbiased view of the population
Summary
Samples are strongly influenced by the information present in the environment [1] In practice this means that samples are practically never truly random, which leads to biases in resulting judgements [1]. Humans often reason through heuristics explaining a simplified version of the world [2]. These simplifications are useful they can result in severe systematic errors [2]. To create an unbiased view of the population these biases have to be addressed and corrected for [1]. Address and provide a method for correcting structural biases in mobile phone data, i.e., mobility data generated from Call Detail Records (CDRs) of mobile providers and scale it to the traveling population
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