With advancements in the photovoltaics (PV) market, involving increased PV module efficiency and reduced costs, the logical progression is the integration of PV into various surfaces, including vehicles (VIPV). For a driving VIPV, the constant change in irradiance presents a significant challenge. The location, weather, and time are key factors that affect the irradiance prediction. An accurate prediction of solar energy generation is crucial for realizing the full potential of VIPV. In this study, the incident irradiance loss is referred to as shading loss.

This thesis is conducted in collaboration with Delft University of Technology (TU Delft) and the Netherlands Organisation for Applied Scientific Research (TNO). TNO has an existing shading loss model that accounts for the surrounding obstructions and weather effects using a fixed value, where the only variable input is the day of the year. On average, it assumes 27% shading losses yearly. This study has improved TNO’s VIPV shading assessment by incorporating more variables into a new model, thereby contributing to a better estimation of the VIPV energy yield. The inputs of the new model are based on key factors causing irradiance loss, where location data is obtained using a Digital Surface Model (DSM). The new contribution to the DSM-based approach is the Web Coverage Service (WCS), which retrieves height data directly through a URL, eliminating the need for the previous manual operations and building upon the model’s scalability. The improved model determines shading losses along a route using two varying shading indicators: Sky View Factor (SVF) and Shading Factor (SF). It can input various irradiance datasets, including or excluding weather effects, and precisely determines the Sun’s position based on the time and day of the year. The DSM-based approach is validated using a dynamic and static validation dataset, with a SVF≤1. The accurate representation of surrounding obstructions during validation contributes to a good-quality model. Both validation studies differ in inputs based on location, time, and weather conditions. The differences in measured and simulated irradiance values are larger in the dynamic dataset than in the static dataset. Deviations are caused by uncertainties and errors, such as local weather effects, sensor inaccuracies, traffic shading, and LiDAR discrepancies, which is the inability to simulate open or changing structures. Weather variability is the primary factor affecting model quality in this validation study, resulting in no fixed inaccuracy factor for model validity. The validated model can accurately represent the environment, but weather variability should be minimized when comparing measured and simulated irradiance. Therefore, averaging is chosen to compare results in this work as it removes unknown or untraceable data points.

Beyond previous research studies, this work focuses on two public transport case studies, given the systematic routing with a fixed timetable. The first case study includes a car and a bus, and the second, more extensive case study focuses solely on buses. The choice of inputs in both case studies centers on key factors influencing VIPV irradiance loss. In the Bus 40 study, simulations under clear sky conditions and a fixed time around solar noon resulted in shading losses of 22.7% for the bus and 27.7% for the car. Moreover, the simulated shading loss shows a 2.6% relative difference compared to the yearly average TNO value. For the case study considering 27 bus routes in Amsterdam, simulations are performed using averaged KNMI irradiance and all hours a day, where the annual average shading loss resulted in 24.8%. The SF and clear sky irradiance correlate in the first case study and the SVF and average KNMI irradiance correlate in the second case study. However, using clear sky conditions and uniform overcast conditions does not represent realistic weather conditions and leads to a bias in the correlation. Moreover, classified terrain types do not correlate with SVF, in contrast to the reversed approach by Araki et al.

Finally, the model can effectively characterize the shading losses on the route level. However, this methodology falls short when reaching finer resolution, such as when examining smaller route segments or partial shading. The determination of shading losses at the route level can be of further benefit to various stakeholders, such as bus operators who want to determine the optimal bus routes for maximum solar energy generation. In addition, this model can be helpful for policymakers, to predict the required charging infrastructure in a scenario when vehicles are equipped with solar panels. Moreover, this model improves shading loss estimation by accepting a range of datasets instead of TNO’s current approach. By inputting irradiance data for clear skies to complete overcast skies, the shading losses can be predicted in a safer range instead of over- or underestimation based on one shading value.

Urban densification is one of the most relevant strategies to reduce the housing shortage in the Netherlands, as it also aims to prevent additional pressure on transportation networks and contributes to more sustainable travel. The effects of the built environment on travel behaviour have been studied extensively. To date, most studies focused on homogeneous effects of the built environment on travel behaviour indicators. Less attention is paid to possible heterogeneity in the effects dependent on other variables. This study applies moderated multiple regression on Netherlands Mobility Panel data to analyse possible heterogeneity of the effects of determinants of travel behaviour on daily travel distance, dependent on the level of urbanisation. The results show that various determinants indeed have effects with varying strengths, dependent on the level of urbanisation. The findings advance the understanding of the relationships between built environment and travel behaviour, which could be used to design tailor-suited policies that efficiently target specific groups. In relation with the built environment, the results show that densification strategies have the desired effects. However, accompanying mobility strategies are necessary to achieve the full potential of urban densification.

The objective of this study is to strengthen literature about cyclist safety and help prevent bicycle crashes in the future. It looks into the gap about the relation between the obstacle space of cycling infrastructure (that involves the space that is available for cyclists to avoid crashes with the obstacles therein) and the visibility thereof, and bicycle crashes. Next to a brief literature research, the main method used to answer this question is modelling of generalised linear models with a negative binomial distribution, that link independent variables concerning the obstacle space to the dependent count variable crashes. Results show that the relation is apparent in data concerning 50 km/h streets in the municipality of Amsterdam: infrastructure on a street level can indeed predict bicycle crashes on the same level. Fewer obstacles next to the road and higher widths were found to lead to fewer crashes. For lanes and one-way tracks holds that an increase in width of smaller sections has a bigger impact. The effect of obstacles in the shoulder also reduces at higher widths. A significant relation for light conditions was not found. Further research should look into different types and clustering of obstacles and improve the light analysis. Recommendations include providing dedicated cycling infrastructure with sufficient widths and few obstacles, and better registration of bicycle crashes. The study demonstrates that infrastructure is an important factor for the occurrence of bicycle crashes, that should certainly not be disregarded alongside the road user and vehicle. It hopes to push infrastructure policies for cycling more on the agenda.

The combination of cycling and public transport (PT) use has been found to be very successful in the Netherlands. On average, 47% of daily train users cycle to a railway station and this number is expected to grow to until at least 2030. As a result, railway stations suffer from bicycle parking capacity shortages. Large investments are needed to meet the growing demand for bicycle parking capacity, while the expansions take up the limited space around railway stations. To address the ever-growing demand for bicycle parking capacity and to continue the success of the combined use of bicycle and PT, alternative solutions beyond building new bicycle parking facilities are needed.
Bicycle sharing is a promising strategy to tackle bicycle parking capacity shortages at railway stations as it could significantly reduce the number of parked bicycles. A fundamental prerequisite is that a bicycle sharing system (BSS) makes use of the already parked bicycles, which is not the case with the available PT-bicycle system in the Netherlands. Existing literature shows that bicycle sharing between current cyclists has the potential, in theory, to reduce the number of parked bicycles by 22-25% at the central railway stations of the four largest cities in the Netherlands. When bicycles are also shared with travellers currently using other modes 37-50% capacity savings could be achieved. Currently there is a lack of knowledge about bicycle sharing from a user’s perspective to make a more realistic estimation of the expected potential. The important factors in bicycle sharing among potential users need to be clarified in order to estimate the potential demand and to progress towards the design of the organisation of an efficient system. Therefore the objective of this study is to investigate the preferences of current bicycle-train users and the demand for bicycle sharing in order to design an efficient bicycle sharing system (BSS).