Bicycles have seen relatively few safety features added since their invention, especially in comparison to the numerous advancements in automotive safety. This thesis investigates the use of simulation to model bicycle dynamics, progressing from a no-slip model to a more complex model incorporating slip, and finally to a model that simulates the interaction of a bicycle with an unexpected patch of ice. Tire slip on ice was estimated using car tire slip data combined with bicycle measurements taken on dry asphalt. The study evaluates the effectiveness of two control strategies, Proportional-Derivative (PD) control, and Linear Quadratic Regulator (LQR) control, through these simulations. The PD controller was tested with gain values ranging from 0 to 20 at regular intervals, while the LQR controller’s weightings were selected through a process of trial and error. The analysis focuses on how variations in PD gains and LQR weightings affect the bicycle’s stability and ability to traverse icy terrain. Results indicate that the LQR controller consistently stabilized the bicycle at an 8° lean angle across all tested speeds and maintained stability at a 12.5° lean angle at higher speeds when crossing ice. Ultimately, the selected LQR controller used diagonal values of 50 and 5 in the state weighting matrix Q, and a value of 1 in the control weighting matrix R.These findings suggest that advanced control strategies, particularly LQR, can enhance bicycle stability in adverse conditions

Riding a bicycle without hands, while being pleasant and sometimes challenging, exposes the self stabilizing properties of the bicycle. While human balancing techniques are widely understood, balancing on a bicycle is predominantly done by turning the handle bars and not many details about body motion control strategies are understood yet. Therefore, creating a rider model that controls the bicycle solely by lateral body motion enables these strategies to be amplified and better appreciated. Only controlling the bicycle by lateral body motion also gives more insight into the relation between roll and steering dynamics as the steering control is non-existent. Further inspiration for the model is obtained by analyzing motion capture data of a rider on a bicycle. This thesis describes the process of creating such a hands-free rider models and the development of simulation tasks to examine this control behavior. These tasks involve performing a double lane switch maneuver, a 90◦ turn and riding through perturbations. The model is created using the BRiM software package, and simulations are performed by optimal control program Opty, which allows for the symbolic equations of the Whipple bicycle model to be used for multi-body-dynamics calculations. After analyzing the control strategies employed by the rider, new insights are obtained on the lateral mechanics of bicycle control, paired with improved joint mechanics that have not been used before in bicycle-rider models. Such new joint mechanism modelling the seat connection between bicycle and rider, that couples lateral translations to body lean proves to be moreeffective than only rider body lean. Also, in terms of optimal control, using a multi-link pendulum model for the rider does not increase the control performance of the rider compared to a single pendulum rider lean model. Furthermore, novel evidence is presented that disproves the assumed need for counter-steering when attempting to make a turn. The rider can make a steady turning motion without having to initially steer in the opposite direction of the turn. Moreover, the effects of placing a spring between the front- and rear frame of the bicycle, both in stabilizing the bicycle as making the bicycle more responsive are investigated. From this it is found that adding a spring will increase the self-aligning properties of the front wheel but that it hampers maneuverability. Finally, the results of this thesis are put in perspective, and recommendations and improvements for future work are shared.

Bicycles are among the simplest and at the same time most fascinating vehicles. Despite their apparently basic design, their stability, dynamics, performance, and safety are still largely unknown. In a world where cities suffer from critical air pollution levels and are often congested by car traffic, bicycles can represent a cost-effective and ready solution to address part of the problem. However, the increasing use of bicycles enlarges the risk of bicycle-related accidents and injuries. Many of these accidents involve falls, highlighting the need of proper studies on bicycle-rider dynamics. While research has been conducted on the effect of bicycle parameters on the handling quality properties, we still do not know enough about the specific role of tyres on bicycle-rider dynamics. To better simulate what happens in the real world, existing bicycle mathematical models need to be integrated with robust tyre models that include side slip, as they can change both the lateral and longitudinal dynamics. Actually, the use of nonlinear tyre models allows taking into account the lateral force and self-aligning torque saturation. This is needed for instance to model the wobble as a non-divergent vibration modes, contrarily to what happens when using a simple linear tyre model. These features relate directly to predict critical situations for which we need a deep knowledge of bicycle tyres, thus proper test-rigs to measure their mechanical characteristics...

In this research a dynamic time and energy model is constructed to simulate free flow utility cycling. Using a Monte Carlo simulation and distributions of rider and bicycle characteristics a population of cyclists is modelled to find the difference in time and energy expenditure on six different bicycle routes. The riders use realistic like power inputs and braking behaviour based on the rider and the route characteristics. While travel times are almost always lower for all cyclists when comparing routes, there are cyclists for who a shorter route distance does not equal a lower energy expenditure. For simpler routes (less elevation difference, traffic lights and shorter distance) the standard deviation of both time and energy decreases, showing that slow cyclist have a relative higher gain. For e-bike users there is even almost no difference in energy expenditure between the six evaluated routes that have varying elevation, traffic signals and routing, while travel times show a similar trend as for regular bicycles. With this model difference in routes can be quantified in matters of time and energy expenditure for a population of cyclists giving an objective picture of the differences between routes, which can be a useful tool for city planning and evaluation of bicycle infrastructure.

Model matching control allows for a virtual change in the vehicle's physical parameters, removing the need to alter the system physically. These vehicles are used as a versatile test bed for safe and repeatable experiments, and allow for safer vehicle operation by virtually keeping the driving behaviour constant in the face of physical changes such as wet roads. Although already applied on planes and cars, it has not been applied to bicycles. The proposed control method accomplishes model matching control by equating the state derivative of the physical system to that of the the virtual system. Theoretical analysis shows model matching control is applicable to bicycle dynamics, even if only the steer torque is available for control. Via experiments, I identify the bicycle's weave mode eigenvalue at different speeds, and the Bode gain between steer torque to lean rate and to fork angle for frequencies between one and three Hz. The experimentally identified values follow the theoretically predicted values closely but do not overlap perfectly. The effect of artifacts during the experiment and unmodelled dynamics are analysed with the use of simulations and are able to explain these perceived differences. From the results, this research concludes that model matching control is applicable to bicycles and is able to virtually change the bicycle's physical parameters. Future work will look into making the controller more robust to modelling errors.

This report describes the development of a Python based tool for 2D structural analysis in civil engineering, using the SymPy library (Meurer A, 2017) and applying the Macaulay method (Macaulay, 1919) to enable symbolic computation for structural analysis. The tool serves as a proof of concept demonstrating the feasibility of symbolic computation in analyzing internal forces. Central to the project is the extension of existing SymPy modules, specifically the Beam module, to support 2D analysis by introducing new modules: "Column" and "Structure2D." The existing "Beam" module in SymPy, which computes vertical load effects, was used to handle shear forces and bending moments by creating a load equation for the vertical direction. To handle normal forces a "Column" module was proposed (although not implemented in this iteration), designed to compute horizontal load effects based on the material’s elasticity modulus, length, and cross-sectional area. The "Structure2D" module integrates both vertical and horizontal load computations by transforming a 2D structure into two corresponding 1D representations. This method enables separate computations for each direction, converting 2D problems into simpler 1D computations while preserving load and support behavior. The tool’s workflow involves users defining structure components (members, loads, supports) on a 2D grid. Once defined, the structure is "unwrapped" to map each member along a continuous line. Each load is split into its horizontal and vertical components and applied to the corresponding axis. Through examples, the report showcases the tool’s ability to calculate shear forces, bending moments, and reaction loads. Although the project successfully demonstrates the potential of symbolic computation for 2D structural analysis, several limitations exist. The tool currently lacks a functional Column module, restricting it to single horizontal reaction loads and limiting its ability to solve for horizontal deflections and axial forces fully. Additionally, the structure must be non-branching and linear, excluding complex configurations like truss networks. Material properties must be uniform across members, a constraint that simplifies the model but limits its real-world applicability. Addressing these limitations would involve developing a more advanced unwrapping algorithm for intersecting members and introducing a complete Column module. In summary, this prototype validates the possibility of a symbolic 2D structural analysis tool, providing civil engineering students and educators with an analytical framework that combines Macaulay’s method and modern symbolic computation. This tool lays the groundwork for future developments that could expand its functionality, to move away from proof of concept to a fully featured open-source product

Bicycles have been studied extensively over the past 200 years, with mathematical models providing valuable insights into various aspects of bicycle dynamics and rider control. However, the lack of a common framework for creating and sharing bicycle-rider models hinders the development of advanced models, research reproducibility, and dissemination. This thesis addresses this gap by introducing BRiM: an open-source modular and extensible framework for creating Bicycle-Rider Models.

The modular setup of BRiM relies on a systematic approach to define a model and form the analytical equations of motion. For the involved analytical computations BRiM utilizes SymPy, a Computer Algebra System. The systematic approach consists of four stages. The first stage defines the objects in the system, such as symbols and bodies. Secondly, the kinematic relationships between the objects, such as angular velocities between reference frames, are established. The third and the fourth stages, which are order-independent, specify the loads and constraints acting upon the system. The decoupling BRiM required to achieve modularity is enabled through this systematic approach, because computations within a stage are mostly order-independent.

The core of BRiM employs the systematic approach within a unified framework for modeling mechanical systems in general. It describes a model using a tree representation, in which a model is defined as an aggregation of smaller submodels. The relationships between submodels are established by parent models, using interchangeable connections to accommodate complex relations, such as tyre models between the ground and a wheel. This application of submodels enables swapping and adding submodels, making the overarching model both modular and extensible. Actuation within BRiM can either be specified by attaching prespecified groups of loads to models and connections, or by utilizing the interface provided by the mechanics module in SymPy, which offers the flexibility to even manipulate equations in detail.

BRiM applies this generalized framework to create modular bicycle-rider models. Both a stationary bicycle and a modular bicycle based on Moore's convention of the Carvallo-Whipple bicycle have been constructed. These bicycle models are extensible to bicycle-rider models by including an upper and/or lower body. Within the rider models each joint can be actuated by a linear torsional spring-damper. BRiM integrates parametrization of models, which provides mappings between symbolic quantities used in equations and experimentally determined values, using the existing open-source BicycleParameters library. Additionally, SymMePlot, a visualization package for symbolically defined mechanical systems, has been developed and integrated within BRiM to visualize the created bicycle-rider models.

The effectiveness of BRiM is demonstrated through optimization and simulation tasks. Firstly, a real-time forward simulation of a torque-driven upper body bicycle-rider is performed. Secondly, an optimization problem is solved, involving the tracking of a rolling disc along a sinusoidal trajectory while minimizing the control torques. These demonstrations highlight the seamless integration of BRiM with other scientific tools and BRiM's potential for practical applications.

In conclusion, BRiM fills the gap in bicycle dynamics research by providing a modular and extensible framework for creating and sharing bicycle-rider models. Its systematic approach, unified framework, and integration capabilities enable efficient model development, research reproducibility, and further advancement in bicycle research.

This study investigated the relationship between Equivalent Fall Height (EFH) and peak Ground Reaction Force (GRF) in freestyle ski jumping. An experiment was conducted in which a semi-professional freestyle skier was instrumented with Inertial Measurement Units (IMUs) and a position sensor. The skier performed jumps off ‘kickers’ in snow parks in four different ski resorts in Northern Italy. The gathered sensor data was utilized to calculate the EFH and GRF of every jump. The data reveals convincing evidence that a positive correlation between these two parameters exists. However, the coefficient of determination, R2, indicates only a moderate correlation. The results suggest that alternating landing strategies might have affected the measured GRF as well. This indicates that a higher correlation between EFH and GRF might exist than shown in this research. Further research should focus on how individual parameters, such as EFH and landing strategy, affect the GRF. This should develop a more comprehensive understanding of how to enhance the safety and enjoyment of freestyle ski jumps for a broader audience.

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.

Most of the injuries sustained by cyclists are due to accidents that do not involve other road-users. This type of accident is known as a single-bicycle accident. A bicycle that is more stable may help to reduce the number of single-bicycle accidents. A balance-assist bicycle is developed at the TU Delft in collaboration with Royal Dutch Gazelle and Bosch eBike Systems. This bicycle has a motor that applies a torque about the steering axis. The motor automatically steers into the direction of the fall - the primary method of controlling a bicycle. The goal of the balance-assist bicycle is reducing the amount of single-bicycle accidents by increasing the stability of the bicycle.

The first research goal of this thesis is to characterize the dynamics of the balance-assist bicycle. Experiments are performed at multiple speeds and controller gains in which the riderless balance-assist bicycle is perturbed and the consequent roll rate is measured. A function describing a decaying oscillation is fitted to this data to retrieve the eigenvalues. A Whipple-Carvallo parameter set is created that approximates the measured dynamics of the bicycle. The results show that the balance-assist controller is not able to stabilize the bicycle when the mass and inertia of a rigid rider is added. However, the controller does damp the weave mode substantially. This likely makes it easier for an active rider to control the bicycle.

The second research goal is to create a model based on experimental data that predicts what perturbations to the rider-bicycle system result in a fall, and what perturbations do not result in a fall. An experimental setup is created that can apply perturbations to the bicycle in a safe environment. The experimental set up consists of four motors that pull on ropes attached to load cells at the end of the bicycle's handlebars. Experiments take place on a treadmill surrounded by padding. Participants wear a safety harness suspended from the ceiling. An operator controlling the treadmill stops the treadmill when a participant falls. The four motors are able to apply perturbations of different magnitudes and direction to the handlebars of the bicycle. The goal of the experiments is to apply perturbations for which a participant falls approximately 50\% of the
time. The controller applying the perturbations and internal sensors of the balance-assist bicycle collect data.

A Bayesian and a frequentist multilevel logistic regression models are developed to predict the probability that a perturbation results in a fall. Results show that there are two predictor variables that have a significant influence on fall probability. Firstly and most importantly, an increase in angular impulse increases the probability that the cyclist falls. Secondly, the fall probability decreases if a participants has experienced more perturbations.

The created model is used to predict the effect that the balance-assist controller has on the fall probability. This shows that the controller could substantially reduce this probability. Moreover, this thesis shows that the balance-assist bicycle substantially dampens the weave mode of the balance-assist bicycle when the mass and inertia of a rigid rider are added. This likely makes it easier for a rider to recover from a perturbation. So, results from this thesis show that, in theory, the balance-assist bicycle could reduce fall probability. The experimental set up, protocol and data analysis scripts developed in this thesis provide an excellent starting point to validate the effectiveness of the balance-assist bicycle in future work.

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.

The rapid adoption of e-bikes as an alternative mode of transportation to automobiles gives rise to new methods of safety regulations for cyclists. Modern e-bikes feature Internet of Things (IoT) modules capable of collecting and sending cycling data that can be used for traffic safety analysis. This study explores the potential of using cycling data to detect dangerous intersections and then implement local low-speed areas using geofencing.

A natural cycling experiment with 10 participants is conducted, where data is collected similar to the capabilities of e-bike IoT modules (GNSS, power and IMU data) and participants are asked to cycle through Delft, The Netherlands. The data is combined with a traffic accident dataset from the Dutch government, where intersections with accidents are labelled dangerous. The cycling data is separated into separate intersection approaches, based on positional data. Metrics based on power and cadence data show the most significant statistical difference (p-values <0.02) and the largest effect size (Cohen's d >0.80) at dangerous intersections. A binary classification model is trained on the dataset, which can correctly predict whether an intersection is dangerous or safe with an accuracy of 68,2%, a specificity of 50,0%, and a sensitivity of 24,2%. This prediction is based on the metrics of a single intersection approach.

To investigate the viability of using geofencing to implement low-speed areas at designated dangerous intersections, a simulation is carried out to determine the minimum size of a geofence that effectively slows down e-bike cyclists. The minimal effective geofence size to limit a cyclist's speed at an intersection is determined by simulating cyclists approaching a geofence for several geofence perimeters, incline levels, and wind speeds. The e-bike's motor stops supporting when the geofence is entered and the cyclist is cycling above a target speed. The minimal effective geofence size ranges from 50 to 300+ m and depends on the target speed, wind speed, and road incline.

This study shows that cycling data can be used to identify dangerous intersections. Enforcing a slower speed on intersections with local geofencing is not feasible, as the geofences have to be extremely large, showing a lot of overlap on other intersections in cities due to high intersection density. Future work can be done on the intersection risk estimation method and the feasibility of speed-limiting geofences that employ active braking.

Bicycle disc brake noise and vibration, which usually falls in the frequency range of 0-2 kHz, is a principal braking quality attribute that has started to attract significant interest from bicycle manufacturers. Brake noise is an irritant to their customers, who see it as a symptom of a defective brake and that leads to unnecessary warranty claims.

Over the years various theories (e.g. stick-slip, sprag-slip, modal coupling) have been developed that explain the noise and vibration generation mechanism in the frictional brakes. Traditionally, research groups working on this issue have conducted their investigations by combining analytical and experimental studies. Likewise, this report aims to predict the brake noise using finite element analysis where the experimental data validates the finite element model.

In order to reproduce the brake noise in the laboratory setting, the brake test machine at the Koninklijke Gazelle laboratory was used. This novel test bench made it possible to run the experiments on the actual bike by providing features to control the bike speed, brake pressure, and weather conditions within the setup. The dynamic characteristics of the brake system were captured with the microphone and the laser Doppler vibrometer. % The audio frequency spectrum was generated by placing a microphone close to the sliding interface of the pads and rotor. A Laser Doppler vibrometer was used to generate the mode shapes and the vibration velocity spectrum of the brake components.
It was established from the results of the brake noise audio and the vibrometer scans that the entire bike vibrated at three peak frequencies during the brake noise events. Brake noise level as loud as 95 decibels was captured. Stick-slip as one of the friction-induced instabilities was identified during the experiments.

Furthermore, a detailed finite element model of the disc brake assembly was developed. Modal analysis on the components of the assembly and a pre-stressed modal analysis on the entire brake assembly was performed. The finite element results compared well with the experimental results. It was observed that there exists doublet modes and intermediate modal coupling in the system.

Limiting the rotor symmetry by adding weights on the rotor ribs was shown to be effective in reducing instabilities. Other ways to counter brake noise by increasing the rotor mass and by introducing a parallel slot in the brake pad were successfully tested.

Cycling is an increasingly attractive transportation mode, thanks to its health and environmental benefits. Personalized travel assistance services can help make cycling more appealing by providing speed or route advices that can reduce travel time and increase safety while taking into account the personal preferences of cyclists. Due to its ability to learn agents' reward function, Inverse Reinforcement Learning is a suitable algorithm for learning cycling preferences from data.
This thesis aims to describe cycling styles as a set of cycling preferences encoded as a reward function composed of a weighted sum of features. The weights associated to the features composing the reward function represent the importance given to each cycling preference and express the trade-off between different goals of a cyclist. Continuous-time Inverse Reinforcement Learning extracts the weights from empirical cyclists' trajectories collected during an experiment performed in Delft. During the experiment, cyclists were asked to cycle according to three different cycling styles: cautious, normal and aggressive. Differences between weight sets extracted for each cycling styles were analyzed by means of the Kruskar-Wallis statistical test and K-Means clustering algorithm, and the averaged weights for each cycling style were used to simulate a set of test trajectories.
It is shown by simulations that the reward function identified for a specific cycling style leads to an improvement in terms of similarity to test trajectories with the same cycling style with respect to the reward functions corresponding to other cycling styles. The statistical analysis shows that the weights of cautious and aggressive cycling styles show statistical differences and define separate clusters.

Skateboarding involves a human controlling a four wheeled vehicle that is steered by tilting the standing surface. The riding mechanics of skateboarding have been well reported [2, 3]. The sport also includes aerial maneuvers such as jumping of stairs, flying off ramps and flipping and rotating the skateboard. The most basic aerial trick is called the ollie. The athlete jumps up while pushing down on the back end of the skateboard’s tail, causing a rotation about the back axle. The upward acceleration due to the rotation together with the tail-ground impact cause the skateboard to go airborne. Midair the athlete drags the skateboard up through frictional contact and levels it out to land the trick. The most concrete performance measure of the ollie is height according to the Olympic judging criteria [4]. To reach maximum height the dynamics such as impact, dynamic response, and torque production are dependent on shape, inertia and mass, which gives reason to assume an optimal shape exists. This leads to the research question: What are the optimal geometric and inertial parameters of a skateboard for an Olympic athlete to reach maximal ollie height. The skateboard geometry is optimized through multiphase direct collocation with the objective of maximal ollie height. A parameterized model is created with scaling mass and inertia properties such that the geometry of the skateboard. Modelling the dynamics of the ollie including impact and friction are done with a point mass human controller that is kineticly and kinematicly mapped to a counter movement jump. A simplistic contact implicit impact scheme is made for a higher order optimization. The ollie height is improved by changing the mass and inertia properties of the skateboard. Multiple optimal board shapes are generated for example a skateboard with a smaller wheelbase can reach higher ollie height compared to an industry standard skateboard.

Commissioned for Team DSM, a computational model for professional road cycling races is developed to answer the question: ``What is the optimal cycling wheel-tire combination, available to Team DSM in order to ride different road cycling races as fast as possible?''. This model is used to make an approximation of the wind that the cyclist experiences, using race data from Team DSM. Using this wind approximation, race times for different wheel-tire combinations are compared. A sensitivity analysis of the model for the coefficient of rolling resistance, coefficient of drag multiplied with the frontal area, mass and rotational inertia is performed. Taking the error in the measurements of parameters into account, the sensitivity analysis and race time results both point to the ``C60 tubeless'' wheel-tire combination to be the fastest combination out of the available wheel-tire combinations to Team DSM. If the weight of a C36 wheel-set was reduced, this wheel set could outperform the C60 wheel-set on mountainous race courses.

Bicycles connected to the internet present an opportunity for integrated accident detection and geolocation. Such a system can reduce the time it takes for help to arrive by automatically alerting predefined contacts with the location of the accident. I developed a systematic method for the practical implementation of bicycle accident detection in connected bicycles and present the performance of a prototype system. The method uses accelerometer and gyroscopic measurements as well as localization and velocity estimations. Supplementing existing research, a bicycle accident detection system is validated on normal cycling, edge cases, and three types of single bicycle accidents with constraints set by a bicycle manufacturer. Edge cases are movements of a bicycle that occur during regular usage, but can not be described by normal cycling. This method uses a data¬driven approach. For the prototype system, the input signals are collected during 71 different simulated accidents and 54 hours of normal cycling and edge cases. A three¬layer detection algorithm determines if an accident has occurred and sends the last known location to a set of predefined contacts. Multiple combinations of thresholds and classification algorithms are compared. This resulted in a prototype system with a K¬Nearest Neighbours classifier which detects 75% of accidents. Normal cycling and edge cases are correctly detected 99.997% of the time. From all warnings send, 85.7% are true accidents. The prototype system proves that the proposed method can be used to integrate reliable accident detection in connected bicycles. Bicycles with such a system automatically inform emergency contacts with a message containing the location of the accident, in a time where every second counts.