The second most used way of transport in the Netherlands is the bicycle. There is a great variety of city bicycles in which each person has their preference. One of the most critical aspects of personal preference is the cycling experience or feeling, better defined as Handling Qualities. Improving the Handling Qualities can lead to more comfort and safety in cycling. The goal is to determine what could improve Handling Qualities which will help within the design stage of bicycles. Handling Qualities are different for each bicycle and depend on many various aspects of the bicycle's geometry. In addition, there are differences in riders' posture and stature that have an influence on the bicycle's control.
To find the most critical parameters that influence the handling qualities, three different bicycles are analysed. Furthermore, three different postures or sitting positions (forward lean) are compared to find the influence of the rider on the Handling Qualities of the total human-bicycle system. In addition, three different statures combined with the right side of the bicycle are compared. A database for human measurements is used to calculate the rigid body of the rider based on an anthropometric model \cite{Moore2009}. The bicycle parameters are gathered from Solidworks bicycle models, given by Gazelle.
A dynamical model is created based on the Whipple bicycle model \cite{Meijaard2007b} with an arm model extension \cite{Schwab2012}. (Steering is the most essential control input).
The eigenvalues are calculated to analyse the stability of each combination. Furthermore, the Handling Quality Metric (HQM) \cite{Hess2012} is calculated to compare Handling Qualities.
As a result of using an arm model, there is no self-stability within any of the rider-bicycle systems. The system can still keep the bicycle upright due to the human arm control of the bicycle. An analysis of eigenvalues shows that the dynamic behaviour of each combination is greatly influenced by the arm model. The different sitting positions and statures also gave a great variety of dynamic behaviour.

The HQM values are greatly influenced by the added arm model, some rider bicycle combinations gave extremely high values. However within high values, there were varying results; for every combination, the HQM values were best for the most upright sitting positions. Furthermore, the best HQM values found are for the tallest person and the worst for a very small person.

Both dynamic behaviour and HQM values are greatly influenced by the added arm model. However, the differences between postures and statures have become clear within this model. An increase in stature has a positive influence on the HQM values as well as a more upright position.
The model can be used in the design stage of the bicycle to predict handling qualities according to the HQM standard. Influential parameters for the desired handling qualities can be found. Different sitting positions and people can be evaluated. Therefore, bicycles can be designed for specific target groups.

Bicycle safety is quickly becoming an increasingly important field as the number of electric bicycles on the streets grows faster each year. E-bikes are able to accelerate quicker and travel at faster speeds than conventional bicycles, increasing the severity of injuries in case of an accident. There are a number of ways to improve safety, such as building better infrastructure, implementing active safety systems, improving bicycling skills, or better protective gear. In this thesis, a controller based on Model Predictive Control has been designed to explore whether it could be used as a haptic guidance system that would improve motor learning of a cycling task, and whether it could assist the cyclist during cycling manoeuvres. The cycling task of lane change manoeuvres performed at a constant forward velocity was investigated. The controller was implemented on a desktop PC, which wirelessly controlled the TU Delft's Steer-by-Wire bicycle -- a bicycle where the steering is enabled by the use of electric motors instead of mechanical coupling between the front fork and the handlebars. To test the controller, ten participants took part in a pilot study. The study was designed following a counterbalanced measures design and the participants were split into two groups that experienced the controller's haptic guidance in different order. During the study, the participants were asked to ride the bicycle on a treadmill and hit virtual targets, shown on a display mounted in front of the treadmill, by carrying out lane change manoeuvres. The hypothesis stated that the controller improves performance while it is assisting the participants. It was found that the controller did not significantly improve immediate performance and this result is likely caused by too low of the task difficulty. However, it is likely that the controller was more effective at improving motor learning of lower skilled participants compared to higher skilled participants, but a small number of lower skilled participants limited the analysis. A short post-hoc no-hands riding test of the same cycling task was carried out to investigate whether the controller is able to carry out lane change manoeuvres with minimal rider input. A significant performance improvement was found during the no-hands test. In conclusion, due to limitations of the study, no performance or motor learning improvement caused by the controller was found. Yet the controller showed promising results in a no-hands riding test, which suggests that the controller could be used as a starting point for advanced safety systems for bicycles.