The relation between flow and density is an essential quantitative characteristic to describe the efficiency of traffic systems. We have performed experiments with single-file motion of bicycles and compare the results with previous studies for car and pedestrian motion in similar setups. In the space-time diagrams we observe three different states of motion (free flow state, jammed state and stop-and-go waves) in all these systems. Despite of their obvious differences they are described by a universal fundamental diagram after proper rescaling of space and time which takes into account the size and free velocity of the three kinds of agents. This indicates that the similarities between the systems go deeper than expected.

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Usually, routing models in evacuation simulations assume that agents have comprehensive and global knowledge about the building's structure. They neglect the fact that pedestrians might possess no or only parts of information about their position relative to final exits and possible routes leading to them. For the sake of a more realistic description of the routing process, we introduce the systematics of using partial spatial knowledge. Particularly, we present an agent-based approach modeling the inaccurate mental representation of pedestrians' spatial knowledge (the cognitive map). In addition, the model considers further principles and constraints of human wayfinding. Furthermore, we present results of a field study we conducted in an office building. The purpose of this study was to investigate route choices of people in dependency on their familiarity with the building. Our modeling approach is then calibrated using the obtained results. In this context, the distribution of routes which were used by the subjects are compared with results of the model.

The current research set out to measure the moderating effect that urban design may have on bicyclist physiology while in transition. Focusing on the hilly City of Wuppertal, Germany, we harnessed bicyclists with mobile sensors to measure their responses to urban design metrics obtained from space syntax, while also adjusting for known traffic, terrain, and contextual factors. The empirical strategy consisted of exploratory data analysis (EDA), ordinary least squares (OLS), and a local regression model to account for spatial autocorrelation. The latter model was robust (R 2 = 68%), and showed that two statistically significant (p < 0.05) urban design factors influenced bicyclist physiology. Controllability, a measure of how spatially dominated a space is, increased bicyclist responses (i.e., decreased comfortability); while integration, which is related to accessibility and connectivity, had the opposite effect. Other noteworthy covariates included oneway streets and density of parked automobiles: these exerted a negative influence on bicyclist physiology. The results of this research ultimately showed that nuanced urban designs have a moderate influence on bicycling comfort. These outcomes could be utilized by practitioners focused on implementing appropriate interventions to increase bicyclist comfort levels and this mode share.