The following paper presents a methodology we developed for addressing the case of a multi-modal network to be implemented in the future. The methodology is based on a simulation approach and presents some characteristics that make a challenge to be verified and validated. To overcome this limitation, we proposed a novel methodology that implies interaction with subjectmatter experts, revision of current data, collection and assessment of future performance and educated assumptions. With that methodology we could construct the complete passenger trajectory Door to door in Europe. The results indicate that the approach allows to approach infrastructure analysis at an early stage to have an initial estimation of the upper boundary of performance indicators. To exemplify this, we present the results for a case study in Europe.
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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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Background: Shared decision-making is one key element of interprofessional collaboration. Communication is often considered to be the main reason for inefficient or ineffective collaboration. Little is known about group dynamics in the process of shared decision-making in a team with professionals, including the patient or their parent. This study aimed to evaluate just that. Methods: Simulation-based training was provided for groups of medical and allied health profession students from universities across the globe. In an overt ethnographic research design, passive observations were made to ensure careful observations and accurate reporting. The training offered the context to directly experience the behaviors and interactions of a group of people. Results: Overall, 39 different goals were defined in different orders of prioritizing and with different time frames or intervention ideas. Shared decision-making was lacking, and groups chose to convince the parents when a conflict arose. Group dynamics made parents verbally agree with professionals, although their non-verbal communication was not in congruence with that. Conclusions: The outcome and goalsetting of an interprofessional meeting are highly influenced by group dynamics. The vision, structure, process, and results of the meeting are affected by multiple inter- or intrapersonal factors.
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