Over recent years, numbers of electric vehicles (EVs) have shown a strong growth and sales are projected to continue to grow. For facilitating charging possibilities for EVs typically two rollout strategies have been applied; demand-driven and strategic rollout. This study focuses on determining the differences in performance metrics of the two rollout strategies by first defining key performance metrics. Thereafter, the root causes of performance differences between the two rollout strategies are investigated. This study analyzes charging data of 1,007,137 transactions on 1742 different CPs by use of 53,850 unique charging cards. This research concludes that demand-driven CPs outperform strategic CPs on weekly energy transfer and connection duration, while strategic CPs outperform their demand-driven counterparts on charging time ratio. Regarding users facilitated, there is a significant change in performance after massive EV-uptake. The root cause analysis shows effects of EV uptake and user type composition on the differences in performance metrics. This research concludes with implications for policy makers regarding an optimal portfolio of rollout strategies.
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Within NHL Stenden University of Applied Science, a choice for a new virtual learning environment was made in mid-2021, primarily on policy and management grounds. Early in the migration process, it became clear that this approach could perturb the further rollout of the Design-Based Education (DBE, https://edu.nl/mwp8j) educational concept. Four templates were developed to intertwine technological and educational processes that structure different ways of "blended" learning and teaching within DBE. Initial user experiences show that the templates’ structures help teachers reconsider online learning activities to shape and facilitate blended DBE learning processes.
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Since the first uptake of electric vehicles, policy makers are questioning how to rollout public charging infrastructure in an efficient manner, such that user convenience balances with costs of investment. In some metropolitan areas, the first phase of rollout has been passed, meaning an optimized deployment of future charging stations for electric vehicles (EVs) becomes important to improve the charging infrastructure and ensure customer satisfaction and sufficient service provision. Complex system literature shows that network vulnerability is an important metric, yet, charging infrastructure has not yet been a subject of these simulation models so far. This research, based on real-world data, provides a novel approach for improving the roll-out strategy of municipalities, by treating the charge infrastructure as a complex network of charging stations and defining vulnerability in respect to the availability of its surrounding charging stations within relevant walking distance.
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Currently EVs constitute only 1% of all vehicles on the road. We are at the eve of the large scale introduction of EVs. Large scale introduction requires a significant growth in charging infrastructure. In an urban context, in which many rely on on-street charging facilities, policy makers deal with a large number of concerns. Policy makers are uncertain about which charging deployment strategy to follow. This paper presents results from simulating different strategies for charging infrastructure roll to facilitate a large scale introduction of EVs using agent based simulation. In contrast to other models, the model uses observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. The simulation incorporates different user types (Inhabitants, visitors, taxis and sharing) to model the complexity of charging in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging), the intensity of rollout (EV to Charging point ratio) and adoption rates. The simulation measures both the success rate and the additional miles cruising for a charging station. Results shows that scaling effects in charging infrastructure exist allowing for more efficient use of the infrastructure at a larger size.
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The transition towards electric mobility is expected to take off the coming years, as more EV car models access the market and charging infrastructure is being expanded. The expansion of charging infrastructure will have to accelerate to keep pace with the fast-growing need for charging. The coming years will be marked by uncertainty regarding technological developments (batteries, range), charging technologies (e.g. fast charging, inductive), growth of car sharing and autonomous driving and impact on user preferences and charging behaviour Data management is key to the EV market and public parties involved: to be able to adapt quickly to changes and to reduce risks and costs. This paper describes the five most important preconditions for effective data management that allows stakeholders to monitor the performance of their charging infrastructure and to take informed decisions on rollout strategies based on data science research results.
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On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.
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With the rise of the number of electric vehicles, the installment of public charging infrastructure is becoming more prominent. In urban areas in which EV users rely on on-street parking facilities, the demand for public charging stations is high. Cities take on the role of implementing public charging infrastructure and are looking for efficient roll-out strategies. Municipalities generally reserve the parking spots next to charging stations to ensure their availability. Underutilization of these charging stations leads to increased parking pressure, especially during peak hours. The city of The Hague has therefore implemented daytime reservation of parking spots next to charging stations. These parking spots are exclusively available between 10:00 and 19:00 for electric vehicles and are accessible for other vehicles beyond these times. This paper uses a large dataset with information on nearly 40.000 charging sessions to analyze the implementation of the abovementioned scheme. An unique natural experiment was created in which charging stations within areas of similar parking pressure did or did not have this scheme implemented. Results show that implemented daytime charging 10-19 can restrict EV owners in using the charging station at times when they need it. An extension of daytime charging to 10:00-22:00 proves to reduce the hurdle for EV drivers as only 3% of charging sessions take place beyond this time. The policy still has the potential to relieve parking pressure. The paper contributes to the knowledge of innovative measures to stimulate the optimized rollout and usage of charging infrastructure.
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Background: This study investigates patients’ use of eHealth services, their awareness of the availability of these services, and their intention to use them in primary care. It also examines patient characteristics and factors that influence the use of these services. Methods: A cross-sectional design using questionnaires was conducted. Based on the unified theory of acceptance and use of technology (UTAUT), the participants rated the two most common services. Descriptive analyses and linear correlation analyses were performed. A simple linear regression was conducted to identify factors influencing the participants’ intention to use eHealth services. Results: In total, 1203 participants with an average age of 43.7 years were surveyed. The participants’ usage rates varied, with the lowest at 2.4%, for measuring vital signs, and the highest at 47.4%, for booking appointments. The intentions to use the services ranged from 22.5%, for video consultations, to 46.6%, for prescription refill requests. Approximately 20% of the respondents were unaware of each service’s availability. Positive associations were found between all the constructs and the intention to use online services, with a younger age being the most significant factor. Conclusions: The use of and intention to use eHealth services varied greatly. The participants were often unaware of the availability of these services. Promoting the availability and benefits of eHealth services could enhance patient engagement in primary care settings.
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Abstract Background Clients with severe mental illness (SMI) have overall poor physical health. SMI reduces life expectancy by 5–17 years, primarily due to physical comorbidity linked to cardiometabolic risks that are mainly driven by unhealthy lifestyle behaviours. To improve physical health in clients with SMI, key elements are systematic somatic screening and lifestyle promotion. The nurse-led GILL eHealth was developed for somatic screening and the imple‑ mentation of lifestyle activities in clients with SMI. Aims of this study are to evaluate the efectiveness of the GILL eHealth intervention in clients with SMI compared to usual care, and to evaluate the implementation process, and the experiences of clients and healthcare providers with GILL eHealth. Methods The GILL study encompasses a cluster-randomised controlled trial in approximately 20 mental health care facilities in the Netherlands. The randomisation takes place at the team level, assigning clients to the eHealth inter‑ vention or the usual care group. The GILL eHealth intervention consists of two complementary modules for somatic screening and lifestyle promotion, resulting in personalised somatic treatment and lifestyle plans. Trained mental health nurses and nurse practitioners will implement the intervention within the multidisciplinary treatment context, and will guide and support the participants in promoting their physical health, including cardiometabolic risk management. Usual care includes treatment as currently delivered, with national guidelines as frame of reference. We aim to include 258 clients with SMI and a BMI of 27 or higher. Primary outcome is the metabolic syndrome severity score. Secondary outcomes are physical health measurements and participants’ reports on physical activity, perceived lifestyle behaviours, quality of life, recovery, psychosocial functioning, and health-related self-efcacy. Measurements will be completed at baseline and at 6 and 12 months. A qualitative process evaluation will be conducted alongside, to evaluate the process of implementation and the experiences of clients and healthcare professionals with GILL eHealth. Discussion The GILL eHealth intervention is expected to be more efective than usual care in improving physical health and lifestyle behaviours among clients with SMI. It will also provide important information on implementation of GILL eHealth in mental health care. If proven efective, GILL eHealth ofers a clinically useful tool to improve physical health and lifestyle behaviours.
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This open access book states that the endemic societal faultlines of our times are deeply intertwined and that they confront us with challenges affecting the security and sustainability of our societies. It states that new ways of inhabiting and cultivating our planet are needed to keep it healthy for future generations. This requires a fundamental shift from the current anthropocentric and economic growth-oriented social contract to a more ecocentric and regenerative natural social contract. The author posits that in a natural social contract, society cannot rely on the market or state alone for solutions to grand societal challenges, nor leave them to individual responsibility. Rather, these problems need to be solved through transformative social-ecological innovation (TSEI), which involves systemic changes that affect sustainability, health and justice. The TSEI framework presented in this book helps to diagnose and advance innovation and change across sectors and disciplines, and at different levels of governance. It identifies intervention points and helps formulate sustainable solutions for policymakers, administrators, concerned citizens and professionals in moving towards a more just and equitable society.
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