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

Johan Meppelink, Jens Van Langen, Arno Siebes and Marco Spruit
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Johan Meppelink: Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands
Jens Van Langen: Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands
Arno Siebes: Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands
Marco Spruit: Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands

Sustainability, 2020, vol. 12, issue 9, 1-19

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.

Keywords: call detail record data; mobile phone data; human mobility networks; trajectory data mining; roadside measurements; scaling method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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