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As it emerges from the literature, bike speed is a trade-off between safety, travel times, and energy expenditure. In addition to that, infrastructure, terrain, and gender play also a key role. By assessing and correlating energy expenditure with cycling speed, it becomes possible to integrate terrain-related factors with individual human capabilities to gauge effort. This paper gathers terrain-related data from sixty-one (61) German cities and uses it to propose a relationship between energy expenditure and bike speed within a macroscopic, physically grounded framework. Furthermore, a Bike Mode Split (BMS) model is introduced to emphasize the role of energy expenditure in predicting cycling demand, as an application of this physically-based framework. Geographic data, Census data, and mode split data are collected from the main official German sources. The result shows that there is a linear relationship between bike speeds and energy expenditure, and also between energy expenditure and slope for conventional and electrical bike (c-bike, and e-bikes, respectively).

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A Macroscopic and Physically-Based Relationship Between Bike Speeds and Energy Expenditure During Commuting Trips

  • 会议论文

  • 发表日期：

    2026年

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  • Giuseppe Cappelli,
  • Sofia Nardoianni,
  • Mauro D’Apuzzo,
  • Heather Kaths,
  • Vittorio Nicolosi,
  • Maria Teresa Iannattone

摘要

As it emerges from the literature, bike speed is a trade-off between safety, travel times, and energy expenditure. In addition to that, infrastructure, terrain, and gender play also a key role. By assessing and correlating energy expenditure with cycling speed, it becomes possible to integrate terrain-related factors with individual human capabilities to gauge effort. This paper gathers terrain-related data from sixty-one (61) German cities and uses it to propose a relationship between energy expenditure and bike speed within a macroscopic, physically grounded framework. Furthermore, a Bike Mode Split (BMS) model is introduced to emphasize the role of energy expenditure in predicting cycling demand, as an application of this physically-based framework. Geographic data, Census data, and mode split data are collected from the main official German sources. The result shows that there is a linear relationship between bike speeds and energy expenditure, and also between energy expenditure and slope for conventional and electrical bike (c-bike, and e-bikes, respectively).