Research article Special Issues

Using huge amounts of road sensor data for official statistics

  • Received: 24 October 2018 Accepted: 11 December 2018 Published: 19 December 2018
  • MSC : 62P99, 62M05

  • On the Dutch road network, about 60,000 road sensors are located of which 20,000 sensors are on the Dutch highways. Both vehicle counts and average speed are collected each minute and stored in the National Traffic Daffic statistics several methodological challenges needed to be solved. The first was developing a method to check and improve the data quality as quite some sensors lacked data for many minutes during the day. A cleaning and estimation step was implemented that enabled a precise and accurate estimate of the number of vehicles actually passing the sensors for each minute. The second challenge was monitoring the stream of incoming and outgoing data and controlling this fully automatic statistical process. This required defining quality indicators on the raw and processed sensor data. The fourth challenge was determining calibration weights based on the geographic locations of the road sensors on the roads. This was needed because road sensors are not uniformly distributed over the road network. As the number of active sensors fluctuates over time, the weights need to be determined periodically. As a result of these steps accurate numbers could be produced on the traffic intensity during various periods on regions in the Netherlands.

    Citation: Marco J. H. Puts, Piet J. H. Daas, Martijn Tennekes, Chris de Blois. Using huge amounts of road sensor data for official statistics[J]. AIMS Mathematics, 2019, 4(1): 12-25. doi: 10.3934/Math.2019.1.12

    Related Papers:

  • On the Dutch road network, about 60,000 road sensors are located of which 20,000 sensors are on the Dutch highways. Both vehicle counts and average speed are collected each minute and stored in the National Traffic Daffic statistics several methodological challenges needed to be solved. The first was developing a method to check and improve the data quality as quite some sensors lacked data for many minutes during the day. A cleaning and estimation step was implemented that enabled a precise and accurate estimate of the number of vehicles actually passing the sensors for each minute. The second challenge was monitoring the stream of incoming and outgoing data and controlling this fully automatic statistical process. This required defining quality indicators on the raw and processed sensor data. The fourth challenge was determining calibration weights based on the geographic locations of the road sensors on the roads. This was needed because road sensors are not uniformly distributed over the road network. As the number of active sensors fluctuates over time, the weights need to be determined periodically. As a result of these steps accurate numbers could be produced on the traffic intensity during various periods on regions in the Netherlands.


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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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