<p>To promote cycling through effective measures, a&#xa0;thorough understanding of&#xa0;cycling behaviour is essential. Researchers and practitioners assume that cycling behaviour, including riding behaviour and route choice, varies across cities. However, there is limited knowledge about these variations, their similarities, and the underlying causes. This article presents the results of a comprehensive analysis comparing riding behaviour and route choice preferences across&#xa0;six German cities. The analysis is based on a large GPS dataset comprising over 200,000 trips. The study reveals notable differences in riding behaviour and route choices between the cities, alongside several shared patterns. These differences may be influenced by urban characteristics such as city size and topography, while the similarities could be attributed to general preferences, attitudes, and adaptive behaviours. The findings provide valuable insights into cyclists' behaviour, enabling cities and municipalities to&#xa0;prioritize cycling promotion more effectively. In some cases, they may also support the implementation and&#xa0;adoption of more generalized strategies. Additionally, the study contributes to the development of city-specific models for predicting cycling demand, optimizing infrastructure, and ensuring the efficient resource&#xa0;allocation for improved network planning.</p>

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Different city = different cycling behaviour? A comparative analysis of cycling behaviour in German cities

  • Stefan Huber,
  • Sven Lißner,
  • Paul Lindemann,
  • Katrin Lubashevsky

摘要

To promote cycling through effective measures, a thorough understanding of cycling behaviour is essential. Researchers and practitioners assume that cycling behaviour, including riding behaviour and route choice, varies across cities. However, there is limited knowledge about these variations, their similarities, and the underlying causes. This article presents the results of a comprehensive analysis comparing riding behaviour and route choice preferences across six German cities. The analysis is based on a large GPS dataset comprising over 200,000 trips. The study reveals notable differences in riding behaviour and route choices between the cities, alongside several shared patterns. These differences may be influenced by urban characteristics such as city size and topography, while the similarities could be attributed to general preferences, attitudes, and adaptive behaviours. The findings provide valuable insights into cyclists' behaviour, enabling cities and municipalities to prioritize cycling promotion more effectively. In some cases, they may also support the implementation and adoption of more generalized strategies. Additionally, the study contributes to the development of city-specific models for predicting cycling demand, optimizing infrastructure, and ensuring the efficient resource allocation for improved network planning.