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).
Cappelli, G., Nardoianni, S., D'Apuzzo, M., Kaths, H., Nicolosi, V., Iannattone, M.t. (2026). A macroscopic and physically-based relationship between bike speeds and energy expenditure during commuting trips. In Computational Science and Its Applications: ICCSA 2025 Workshops (pp.335-349). Cham : Springer [10.1007/978-3-031-97654-4_21].
A macroscopic and physically-based relationship between bike speeds and energy expenditure during commuting trips
Cappelli, Giuseppe;Nicolosi, Vittorio;
2026-01-01
Abstract
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
