This project examines the practices of six Norwegian municipalities, which in a va-riety of ways are trying to enable mentally disabled residents to build or buy their own homes. The survey has confirmed some previous findings: the financing model operat-ed by the Norwegian State Housing Bank makes it possible for many mentally disabled persons to purchase homes on the ordinary housing market. However, even though private developers are without exception friendly, this appears to be a complicated and resource-demanding process. The cases show how municipalities can enable better processes in different ways. Leaving home can be a major upheaval both for young mentally disabled people and their parents, entailing changes in the young person’s re-sponsibilities, personal economy, and sense of independence. The overall objective is achieving inclusivity. Some of the homes are quite ordinary and are located among other similar homes, but our findings indicate that to date, residents experience little or no positive interaction with their other neighbors. Natural meeting places and commu-nal areas dedicated to entire neighborhoods occur only rarely in new housing projects. If we are to meet the objectives of inclusivity and participation, we have to expand our focus and not only look at the homes but also at the neighborhood in which the resi-dents shall be included.

Optimizing the performance of racing motorcycles is a central goal for competition teams. The necessity to ensure driver stability and a good level of grip in the widest possible range of riding conditions makes it necessary for tires to work in the right temperature window, capable of ensuring the highest interaction force between tire and road. Specifically, the internal temperature of the tire is a parameter that can be difficult to measure and control but has a significant impact on motorcycle performance and, also, on driver stability. Deepening knowledge of internal tire temperature in racing motorcycles can improve performance optimization on the track and finding the right motorcycle setup. In this work, a physical thermal model is adopted for an activity concerning the development of a moto-student vehicle, to predict the racing motorcycle setup allowing the tire to work in a thermal window that optimizes grip and maximizes tire life. More in detail, a focus has been placed on the effects of the motorcycle’s wheelbase and pivot height variations on internal tire temperatures. Indeed, the stability and handling of the vehicle are highly dependent on the geometric properties of the chassis. Several values of such quantities have been tested in a properly implemented vehicle model developed in the “VI-BikeRealTime” environment, validated by outdoor tests, able to provide forces acting on the tires, slip indices, and speeds, needed by the thermal model as inputs. Through the analysis of the internal temperatures calculated by the model, reached by the various layers of the tire, it has been possible to investigate which of the simulated conditions cause a too-fast thermal activation of the tire and which of them can avoid overheating and underheating phenomena. Lately, this research has delved into the correlation between motorcycle riders' paths and temperature fluctuations with the aim of comprehending how minor alterations in routine maneuvers may influence tire energy activation, particularly in the context of racing and qualifying conditions.

Active safety systems for powered two wheelers (PTWs) are considered a key pillar to further reduce the number of accidents and thus of injured riders and fatalities. Enhanced awareness for the current riding situation is required to improve the performance of current systems as well as to enable new ones; this includes the detection of the rider’s intention – the action that is planned by the rider for the short-term future. The prediction of a continuous trajectory for the upcoming seconds of the ride is one way to express rider intention. Our work pursues the prediction of the PTW lateral dynamic state by means of a roll angle trajectory over the upcoming 4 s of riding. It thus considers the special vehicle dynamics characteristics of single-track vehicles that negotiate bends at a roll angle compared to cars. A deep learning (DL) prediction model that is based on a Long-Short Term Memory (LSTM) layer is optimized and trained for this task using a broad on-road riding dataset that focuses on the rural road environment. Inputs to the prediction model are PTW internal signals only, that are measurements of vehicle dynamics, rider inputs, and rider behavior. The latter two groups of signals are non-common for current series production PTWs and were especially added to our test bike before gathering the riding data set. The prediction performance of the optimized DL model is compared to a simple heuristic algorithm using multiple metrics in the roll angle and position trajectory domain. Evaluation on a representative test data set shows a significantly improved detection of rider intention by the DL model in all metrics. Reasonable lateral trajectory accuracy is achieved for at least 2 s of the total 4 s prediction horizon in 99 % of all test cases, given the chosen model architecture and input features. Furthermore, the feature importance of the especially added non-common measurement signals of steering and rider behavior is investigated in an ablation study. It reveals the importance of steering signals in the first second of the prediction horizon whereas the rider behavior signals aid trajectory prediction performance for up to 2.5 s.

International and prestigious universities located in small cities are growing at a rate beyond the spatial capacity of their host city. Due to this, the presence of students and student housing in these cities has exponentially grown and resulted in a myriad of social, cultural, economic, and spatial impacts. This is known as ‘studentification’ which affects the co-existence and tolerance between the university community and the local community, particularly between students and local residents who lead entirely different and clashing lifestyles. Existing research on campus-city relationships has primarily focused on the economic benefits of large universities in small cities, whereas research on student housing in the Netherlands has primarily focused on the shortage for both incoming and existing students. However, much less attention has been given to the current conditions of student housing qualitatively and what student housing typologies mean for other residents in Delft that may have the potential to shift perspectives from student growth to limitation. As tensions are at an all-time high and further expansion growths have been announced by TU Delft, a need to understand the fundamental conditions that contribute to the negative consequences of studentification is needed more than ever to thoroughly understand the studentification process and recommend a long-term strategic plan towards co-existence. This policy brief highlights the effects of student housing in Delft and creates a strategic plan that is informed by practices of other European cities (Lund, Gottingen, and Loughborough) that is viewed through the political, spatial, and sociocultural lens of Delft to provide an evidence-based and comprehensive approach that transcends conventional practices. By proposing a pathway of policies, regulations, and strategies, a step-by-step process of mitigating the different conflicts and issues resulting from studenification may be mitigated.

Over the last decade, solar energy has proven to be a key technology in transitioning to a sustainable energy system. However, current solar energy policies favour affluent households, limiting the participation of disadvantaged households in the energy transition. This leaves disadvantaged households even more vulnerable to increasing energy costs, as the recent unprecedented rise in energy prices has painfully demonstrated. To ensure that transition mechanisms are accessible to all households, solar energy policy needs to consider spatial justice. With this perspective, we go beyond technical analyses of solar energy potential and use a socio-spatial approach to evaluate the adoption of solar energy in The Hague. This policy brief is based on a research study that evaluated the transition to solar energy in the city of The Hague, The Netherlands, from a spatial justice perspective. Through a socio-spatial analysis at the postcode level, the research identified four distinct groups with varying levels of access to solar energy. The results show that these groups are not only strongly segregated across the city but also overlap with existing socio-spatial inequalities. The four levels of access to solar energy are then compared to current solar adoption rates and technical rooftop energy potential in the city. Results show that decreasing levels of access to solar energy align with decreasing adoption rates, revealing that current policies fail to provide equitable access to solar energy, leading to inequalities in adoption rates. Furthermore, the study quantifies how much of the technical potential available in The Hague is in areas where access to solar energy is limited, revealing a significant amount of untapped technical potential with the potential to address existing socio-spatial inequalities. Finally, two groups of interest and related leverage points for future policy interventions to address equity in the transition to solar energy in The Hague were identified.

Energy poverty is a pressing issue in the Netherlands, with the number of households struggling to cover their energy bills doubling to nearly one million in recent years. Current policies and subsidies have failed to address the needs of underprivileged social groups, leaving them vulnerable and unable to access support for dwelling renovations. This policy brief emphasises the importance of adopting a socio-spatial approach to tackle energy poverty and incorporate justice into renovation policies. By understanding the underlying factors that contribute to vulnerability and pinpointing their spatial distribution, targeted policies can be developed to meet the unique needs of vulnerable groups. The brief highlights the systemic challenges in Amsterdam Zuidoost, where low incomes, lack of trust, and financial constraints hinder renovation efforts. It stresses the urgency of adopting a spatial perspective, recognising the socio-spatial dimensions of vulnerability, and engaging local communities. Through inclusive and participatory processes, the brief aims to promote social equity, spatial justice, and sustainable solutions to combat energy poverty in Amsterdam Zuidoost.

São Paulo, a city characterised by rapid urbanisation, long-term governmental neglect, and a widening societal gap, faces a complex challenge. Communities within the city experience extreme multidimensional inequality, marked by socio-economic vulnerabilities which stem from their marginalisation from society. In addition, the degradation of critical ecological systems and mounting climate change pressures intensify the risks of environmental disasters, creating a situation of extreme double exposure. This means that vulnerable communities in São Paulo contend with not only socio-economic vulnerabilities but also face heightened environmental risks. While Brazil’s policies took significant steps in the 1980s with the creation of the Estatuto da Cidade (City Statute in 2001) to address social inequality, current solutions are still unable to adequately reduce the multitude of vulnerabilities that marginalised communities face. In order to ensure a high standard of liveability and foster an environment of resilience for São Paulo communities, a comprehensive analysis that exposes the factors that contribute to the creation of multi-dimensional inequality is required. Firstly, recognising that inequality is not just a matter of income but is intricately linked to spatial and environmental dimensions. This policy brief calls for a holistic approach that recognises the interconnectedness of socio-economic vulnerability and environmental risks. By adopting the principles of a socio-ecological approach and conducting a comprehensive socio-spatial analysis, São Paulo can chart a path towards a more equitable and sustainable future for all its residents.

Understanding and mastering handling quality is a critical concern for bicycle designers, as it directly impacts safety, comfort, and performance. However, this aspect has received limited attention to date. Existing literature offers experimental handling quality indicators based on bicycle kinematics, but their validity has yet to be established. This study aims to assess the predictive power of these indicators using experimental data derived from subjective assessments of handling quality. These data, obtained from a protocol involving 20 participants and 2 bicycles, enabled testing 39 experimental indicators. The results indicate that certain vehicle kinematic quantities are indeed correlated with the perception of handling quality but with low predictive power. Indicators based on handlebar movement are the most effective in explaining the sensation of handling quality. These indicators perform particularly well at low speeds, where physical and cognitive workload are associated with the quantity of control actions on the handlebars.

Based on the understanding of the built environment as result of competing claims on space that must be resolved via recognition, fair distribution of burdens and benefits of our human association, respect and care for the planet and just procedures to decide on those claims, Spatial Planning and Strategy is a chair in the Department of Urbanism within the Faculty of Architecture and the Built Environment of the Delft University of Technology, committed to helping create sustainability, resilience and spatial justice through the implementation of the New Urban Agenda, the Paris Climate Agreement and the European New Deal, among other frameworks. This commitment is reflected in activities, events, and courses. We are concerned with knowledge about the formulation, implementation, and evaluation of strategic and urban planning tools – visions, strategies, plans and programmes.

To develop advanced motorcycle assistance systems, the focus is shifting towards the rider's abilities. A model in (Scherer et al. 2022) predicts motorcycle dynamics influenced by riders without specific rider or vehicle parameters. It employs mathematical functions to describe speed and roll angle changes, revealing differences among riders. Unlike previous stochastic approaches, this model allows clear interpretation of measurement data with rider-specific parameters like correction amplitudes and trends, aiding critical maneuver identification. The paper investigates applying this rider model to real traffic data. For this purpose, three riders (two experienced frequent riders and one inexperienced infrequent rider) on two different vehicles (Honda CBF 1000 and BMW K1200R Sport) were recorded and examined on a sample basis using a validated low-cost measurement technique with a total amount of n = 40 measurements. Taking into account evaluation curves suitable for proving the methodology, two consecutive country road curves were selected with a respective change in direction (equivalent to a yaw angle change of the vehicle between entering and exiting the curve) of approx. 180°. These were each driven through 5 times by all three riders under constant conditions in good, summer weather and road conditions. In addition, one of the riders drove through them in wintry and less than optimal road conditions at the beginning of the season. Initial findings assess the model's transferability to real traffic. The investigation results show its applicability, with rider-specific riding styles and parameterization functions, as well as the need to repeat the study with a large number of samples. The model accurately predicts future positions, with over 85% of maneuvers having less than a 2% lateral deviation. This demonstrates applicability under real conditions, confirming its efficacy beyond the closed terrain test in (Scherer et. al., 2022). In the future, this model will enable rider-dependent trajectory predictions with uncertainty intervals in real traffic situations.