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This paper attempts to predict average daily bicycle volumes on a nationwide level using crowdsourced GPS data from the CITYCYCLING campaign in Germany. The data source was 514 permanent counting sites across the country as well as the campaign-generated GPS bicycle volumes of about 300,000 participants and 7.5 million tracks from a smartphone app. For model building, Gradient Boosting Regression and Support Vector Regression were selected. The results show a medium to high model fit for the prediction of bicycle volumes at sites with permanent counters. To illustrate this, the models are applied to the road network of a district of the city of Dresden, Germany.

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Network-Wide Estimation of Average Daily Bicycle Traffic Based on Crowdsourced GPS Data and Permanent Counters

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Emely Richter,
  • Joscha Raudszus,
  • Sven Lißner,
  • Iwan Porojkow

摘要

This paper attempts to predict average daily bicycle volumes on a nationwide level using crowdsourced GPS data from the CITYCYCLING campaign in Germany. The data source was 514 permanent counting sites across the country as well as the campaign-generated GPS bicycle volumes of about 300,000 participants and 7.5 million tracks from a smartphone app. For model building, Gradient Boosting Regression and Support Vector Regression were selected. The results show a medium to high model fit for the prediction of bicycle volumes at sites with permanent counters. To illustrate this, the models are applied to the road network of a district of the city of Dresden, Germany.