The fifteen-minute city (FMC) concept aims to reduce greenhouse gas emissions, improve urban liveability and free up space by promoting a modal shift from car to active modes. According to theory, the proximity of all basic needs should have a maximum travel time of 15 minutes by bike or foot to achieve the FMC goals. However, basic needs are poorly specified and discussed, the effectiveness of the FMC concept is unknown and difficult to measure by current indicators. Therefore, this paper clarifies the FMC concept. It statistically analyses the effect on the promotion of active modes by logistic binary regression, applied to the Rotterdam-The Hague metropolitan region. It introduces FMC indicators to measure and geographically analyse urban regions. It reveals a set of amenities that characterises the FMC. Further, it demonstrates that the FMC concept has a positive effect on the probability of making trips by active modes, but also demonstrates the effects of other relevant variables, such as car ownership or the quality of cycle paths. Moreover, an FMC indicator that combines both the proximity of amenities and the number of amenities within 15 minutes predicts the effect of the FMC concept best. The FMC concept is beneficial to promote active modes, but to be most effective, active modes should also be stimulated via other measures, including the improvement of the quality of cycle paths and discouragement of car ownership for areas with all FMC amenities in close proximity.

In order to stop climate change, it is inevitable to transition to more environmental-friendly ways of transportation, such as cycling. An important criterion for selecting the bicycle as a modality is the distance to the destination. An increase in travel distance coincides with a decrease in modal share of bicycles compared to alternative modalities. Literature does not explain why cycling is preferred for certain distances and not for others, except for some practical reasons. This thesis investigates the built environment in relation to cycling distance. Literature indicates the built environment as being important for destination choice, mode choice and route choice. Moreover, literature also indicates a relationship between cycling distance and these decision choices. As urbanization of the built environment increases, more destinations are within reach and this tends to reduce travel distance. At the same time, network friction reduces due to increasing network density. Finally, preferences to cycle through or avoid certain characteristics of the built environment may result in detours, which increase travel distance. People’s decisions on travel destination, transportation mode and route appear to be crucial for choosing the bicycle as mode of transport. Humans make these decisions simultaneously, but in this thesis, it is assumed that it is done in the order: destination, mode, and route. By making this assumption, it is possible to say that (a) destination choice is connected to Euclidean distance, (b) mode choice is connected to network friction, and (c) route choice is connected to behavioural detour. The 7D’s framework of Ewing & Cevero (2010) has been used to quantify the built environment. It will turn out that only the first three D’s (Design, Density and Diversity) are influencing cycling distance. The choices for destination, mode and route choice are also influenced by the following elements: network density, cycle paths, green environments, smooth surface, built environment density and the mixture of functions in the built environment. This research has been conducted with bicycle trip data within the municipalities of Amsterdam, Amstelveen, Diemen and Ouder-Amstel. Those municipalities offer a fitting case study, since the ‘Greater Amsterdam’ has a varied built environment and has a need to reduce car traffic significantly. In this thesis, the relation of the elements with cycling distance are statistically tested with a multiple linear regression analysis. In order to do so, elements have to been quantified and combined with the bicycle trips. Explanatory models are developed to explain cycling distance, Euclidean distance, and detour distance with the elements of the built environment. The models constructed in this thesis show that the elements green environments, waterbodies, smooth surface material, and cycle paths have a positive increasing impact on cycling distance, while network density and built environment have a negative decreasing impact. No clear impact on travel distance is found for the element mixture of functions. This thesis also show that the built environment is more important for determining the Euclidean distance than its importance detour distance. The results from this thesis confirm the relationship between built environment and cycling distance. To be specific, the results show that the built environment density is most important for Euclidean distance and therefore destination choice. Green environments and waterbodies are incentive elements of the built environment to overcome the distance added by the network friction. The two elements can also affect the cycling distance due to the choice of behavioural detour. This shows that bicycle use for longer distances can be stimulated by adding more green and potential waterbodies in the built environment.

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.

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.

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.

This study examines the extent of which residential location and workplace location affect commuting duration and commuting distance. Moreover, by splitting to income groups social equity issues in commuting in connection to rising property values in Amsterdam come to the fore. The report employs data from the 2010, 2011, 2016 and 2017 OViN surveys to investigate changes over a time period of five years. The findings from the spatial analysis, data analysis and regression models indicate that not low income households but instead middle income households more often live less central than high income households. Middle income commuters did not reduce their commuting duration and commuting distance by moving away from the city centre. Both increase when they reside less central. Low income commuters, however, did improve their commute and now commute shorter than middle and high income commuters. When working outside Amsterdam commuters who reside central in Amsterdam but not central outside Amsterdam experience the shortest commutes. People who reside not central outside Amsterdam experience shorter commuters than those who reside central outside Amsterdam. Given the findings described in this report a new debate could arise in which not the worsening opportunities of low income households should be prioritised, but instead should give way to the worsening conditions of middle income households. Future research should focus on personal preferences of individual commuters. In this research the assumption was made that every respondent that lives in Amsterdam choses to do so. This assumption cannot be made for people living elsewhere, because they may not want to live in a dense urban area. By integrating the personal preferences of respondents the different groups can be better compared.

The outbreak of the COVID-19 pandemic in 2020 seriously impacted the world. Some previous researches suggest that global incidents could strongly influence the demand for passenger air transport services (Sobieralski, 2020). And the impacts on the demand can be ascribed to the change in the air travel behaviour of travellers (Ito & Lee, 2005). However, the influence of a pandemic on passengers’ air travel behaviour has been rarely studied. This thesis studies the differences in air travel behaviour between the COVID-19 period and the non-pandemic period, in order to benefit the decision-making of policy-makers.In this thesis, it is assumed that the COVID-19 pandemic influences air travel behaviour through changing the air transport context. Three context aspects are found to influence how air travellers behave during the pandemic. Firstly, the policies including travel restrictions and travel recommendations imposed by the government discourage travel motivation. Secondly, the rise in public risk perception triggers their health-protective behaviour which leads to low willingness to fly or changes in travel choice decision-making. The operation adjustments of the airlines in response to the COVID-19 pandemic also influence the offered flight options in the market which could change flight choice behaviour.Two important air travel choice behaviours during the COVID-19 pandemic are further studied, which are the choice between flying and not flying (willingness to fly) and the choice between flight alternatives. Stated choice data is used since it avoids the bias of revealed choice data in studying willingness to fly. Discrete choice modelling is used because of its advantage in studying individual choice.Air travel convenience and airborne health safety are considered important to air travel choice-making during the pandemic. Therefore, they are added as two attributes in the stated choice experiment. The perception of them is scaled by the perception rating experiment using the Hierarchical Information Integration theory. The results show that the perception of air travel convenience is influenced by the requirement of health-related paperwork, such as health statement and virus test result, for travelling and the travel flexibility, such as change and return policy given, by the airlines. The perceived health safety is influenced by the intensity of the virus precautionary measures implemented in the cabin.The results of the stated choice experiment show a considerable change in the attribute trade-off between flight alternatives made by air travellers during the pandemic. Perceived health safety is the foremost factor that air travellers consider. If an intermediate serious pandemic is assumed, air travellers are willing to pay at least 100% more money to improve the virus precautionary measures by 1 level compared to the non-pandemic situation. This percentage difference becomes larger when the safety level Increases to a higher level, which means that air travellers are more willing to take the safest flight during the pandemic. Transfer and flying time are considered as other two important attribute during the pandemic because they indicate the chance of the exposure to the risky environment. Therefore, travellers are willing to pay 87.9 euro during a serious pandemic to reduce 1 transfer, while they would only pay 42.6 euro during the non-pandemic period. Correspondingly, the ticket price of the flight becomes less important in the exchange for a better performance of other attributes.The willingness to fly is significantly deteriorated by the COVID-19 pandemic. In the case of a serious pandemic, the willingness to fly reduces from 83.5% to 37.6% if the flights in the market have a medium attribute performance. Although improving several flight attributes including price, transfer, travel convenience and health safety can pull up the willingness to fly, however, the negative impact of the pandemic is so strong that even the most extreme attribute improvement, ignoring the profitability and sustainability, still cannot totally offset the willingness to fly reduction because of the COVID-19.The following recommendations are given to policy-makers. Firstly, meeting the customers’ expectation of health safety is the best way for airlines to compete during the COVID-19 pandemic. Two methods to do so include providing non-stop flights as much as possible and improving the intensity of virus precautionary measures in the cabin. Reducing the price is still effective in attracting customers, but less than during the non-pandemic period. Secondly, airlines can increase their load factor by improving flight attributes. But the negative impact of the virus cannot be fully compensated. Thirdly, because the willingness to fly is strongly related to the seriousness of the pandemic, the government should never underestimate the rebound of travel motivation in order to avoid the uncontrollability of the pandemic when the situation gets better. Fourthly, as the COVID-19 is unlikely to disappear soon, the low willingness to fly will remain and threaten the survival of airlines. The governments should work out a sustainable financial aid plan with airlines that can also balance the other goals for the society including economic growth and environmental protection.

In the Netherlands, many cities aim to promote cycling as a solution for sustainable mobility. However, the bicycle parking shortage is common. The popularity of ‘car-free city’ concept provides the opportunity to solve the problem by replacing car parking spaces to bicycle parking spaces. This study used a GIS-based location-allocation method to optimally locate the bicycle parking facility in car parking spaces. Through a case study in the city centre of Leiden, the results show with the small amount of car parking spaces, bicycle parking shortage can be largely relieved.

The acute obstetrics (AO) sector in the Netherlands faces two challenges. The increasing difficulty to find personnel and the closing down of several locations have put a major pressure on the existing AO locations. This has led to questions about the quality of the spatial distribution. The measures currently in place are based the travel time to the closest hospital. However, the recent developments have forced AO locations to say ‘no’ when asked to take care of a woman in labour. In other words, the unavailability of beds in the nearest hospital. The current accessibility measures cannot capture this and therefore the concept of substitutability is introduced. The mathematical model for substitutability offers a possibility to assess the effect of elimination of the preferred option. The input parameters for the substitutability calculations have been derived from a stated choice experiment among 151 female residents of the Netherlands. The cost and time parameters were estimated using a multi nominal logit model. A comparison between minimum travel time, LogSum accessibility values and normalised substitutability values guided the assessment of the spatial distribution. This comparison provided insights in new critical areas of the network. The study serves as an exploration of substitutability and it has proved it to be a promising concept for future research.