Studies of bicycle traffic characteristics are essential for understanding bicyclist behavior and developing suitable microscopic models for traffic simulation. Based on empirical data on real-world bicycle traffic, obtained through video-based data collection techniques, we describe characteristics of free-riding, following, and overtaking that facilitate the simulation of bidirectional bicycle traffic. In total, we analyze data from 195 605 bicyclists across seven bicycle paths in Sweden, with five located in Stockholm and two in Gothenburg. The analysis reveals multimodal distributions of speed and lateral position due to the distinct preferences of bicyclists that vary according to the type of bicycle used. Moreover, speeds are generally highest during morning rush hours at the locations under investigation. Based on wind measurements, we conclude that there is no statistically significant effect on mean free speed from wind speeds in the range of +/- 3 m/s. The distribution of speed differences between bicyclists at overtakings indicates a broad range of speed disparities among bicyclists, and is significantly influenced by infrastructure design. Furthermore, infrastructure design (path width, horizontal alignment, and presence of fixed objects along the edge) and traffic volumes significantly lateral positioning. Our results demonstrate the inherent heterogeneity in the characteristics of bicyclists, underscoring the need to advance modeling to incorporate these distinct characteristics into microscopic traffic simulation.

Microscopic traffic simulation is a useful tool for the planning of motorized traffic, yet bicycle traffic still lacks this type of modeling support. Nonetheless, certain microscopic traffic simulators, such as Vissim, model bicycle traffic by applying models originally designed for car traffic. The gradient of a bicycle path has a significant impact on the speed of cyclists; therefore, this impact should be captured in microscopic traffic simulation. We investigate two calibration approaches to reproduce the effect of gradient on the speed of cyclists using the default driver behavioral model in Vissim. The first approach is to modify the simulated gradient to represent different values of the gradient-acceleration parameter: a fixed value that represents a decrease in the maximum acceleration that cyclists can apply on an uphill. The second approach is to adjust the maximum-acceleration function. We evaluate both approaches by applying a Vissim model of a bidirectional bicycle path with a 3% gradient in Stockholm. The results show that the current default implementation in the Vissim model underestimates the effect of gradient on speed. Moreover, the gradient-acceleration parameter does not directly reduce the maximum acceleration of all cyclists, but only of those cyclists riding above a certain speed. We conclude that by using a higher gradient-acceleration value than the default, we accurately estimate the observed mean speed on the uphill. However, neither of the investigated calibration approaches provides accurate estimates of the speed distributions. We emphasize the need for developing more accurate behavioral models designed for cyclists.

To simulate bicycle traffic accurately, it is essential to capture how bicyclists react to features of the infrastructure such as the longitudinal gradient of a bicycle path. Bicycling requires human-powered motion, and the power output provided by bicyclists differ significantly among bicyclists due to physical capabilities and preferences. Therefore, the objective of this paper is to investigate the connection between power output and gradient in bicycle traffic, with the purpose of developing a power-based model that predicts accurately the speed of bicyclists. Based on trajectory data of free-riding bicyclists travelling on a non-flat bicycle path segment, we estimate changes in power output as a function of gradient considering the physical forces acting on a bicycle. The results suggest a linear correlation between power output and gradient; while bicyclists increase their power output on the uphill as gradient increases, they decrease their power output on the downhill as gradient increases. By implementing this correlation into a traffic simulation algorithm, we show that the simulation captures well the impact of gradients in a population of bicyclists as it reproduces similar speed profiles. We conclude that bicyclists adapt their power output to compensate for the gradient and its associated change in speed, and that the impact of gradient varies greatly among bicyclists. Furthermore, we conclude that power-based modelling of free-riding bicyclists is an attractive alternative to investigate further.

Understanding free riding behavior-where bicyclists are unconstrained by other road users or traffic control measures-is essential for planning efficient and appealing bicycle traffic systems. Bicyclist behavior is shaped by a combination of environmental conditions and individual preferences. This study examines free riding behavior, and identifies correlations with individual characteristics and contextual features such as infrastructure design (slopes and curves) and wind speed. We introduce a method using instrumented bicycles in a semi-controlled experiment to collect data describing the speed, power output, and heart rate of commuting bicyclists. Participants in two study populations (28 in Sweden and 29 in Germany) ride their bicycles equipped with sensors along designated routes during off-peak demand periods, enabling comparative analysis of different trip features. Results highlight significant inter-and intrapersonal variations in speed and power output along a trip. Approximately 80 percent of the variation in free riding speed and power output over a trip, and over both populations of bicyclists, is explained by gender, individual preferences, topography, curvature, crossing intersections, and wind speeds. Headwinds and uphills generally reduce speeds but bicyclists increase power output to partially offset these effects. Downhills lead to high speed variation and distinct tactical behaviors, such as braking, coasting, and accelerating. These findings underscore the complexity of bicycling behavior and quantify how bicyclists adapt to varying features of the trip.