This toolkit, originating from the research group Psychology for Sustainable Cities, Amsterdam University of Applied Sciences (AUAS), contains materials that help to promote behavioural change in relation to electric shared transport based in onstreet e-Mobility hubs (eHUBs). Behavioural knowledge is an essential ingredient for the successful implementation of eHUBs. Because behaviour is very dependent on the target group’s capabilities and motivation and on the social and physical context in which behaviour takes place, the research group has developed materials that municipalities can use to design a tailor-made eHUBs promotion intervention that suits their own situation. Therefore, practical examples and insights from earlier research are shared with regard to stimulating the use of eHUBs.
BACKGROUND: Mobility is a key determinant and outcome of healthy ageing but its definition, conceptual framework and underlying constructs within the physical domain may need clarification for data comparison and sharing in ageing research. This study aimed to (1) review definitions and conceptual frameworks of mobility, (2) explore agreement on the definition of mobility, conceptual frameworks, constructs and measures of mobility, and (3) define, classify and identify constructs.METHODS: A three-step approach was adopted: a literature review and two rounds of expert questionnaires (n = 64, n = 31, respectively). Agreement on statements was assessed using a five-point Likert scale; the answer options 'strongly agree' or 'agree' were combined. The percentage of respondents was subsequently used to classify agreements for each statement as: strong (≥ 80%), moderate (≥ 70% and < 80%) and low (< 70%).RESULTS: A variety of definitions of mobility, conceptual frameworks and constructs were found in the literature and among respondents. Strong agreement was found on defining mobility as the ability to move, including the use of assistive devices. Multiple constructs and measures were identified, but low agreements and variability were found on definitions, classifications and identification of constructs. Strong agreements were found on defining physical capacity (what a person is maximally capable of, 'can do') and performance (what a person actually does in their daily life, 'do') as key constructs of mobility.CONCLUSION: Agreements on definitions of mobility, physical capacity and performance were found, but constructs of mobility need to be further identified, defined and classified appropriately. Clear terminology and definitions are essential to facilitate communication and interpretation in operationalising the physical domain of mobility as a prerequisite for standardisation of mobility measures.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
298 woorden: In the upcoming years the whole concept of mobility will radically change. Decentralization of energy generation, urbanization, digitalization of processes, electrification of vehicles and shared mobility are only some trends which have a strong influence on future mobility. Furthermore, due to the shift towards renewable energy production, the public and the private sector are required to develop new infrastructures, new policies as well as new business models. There are countless opportunities for innovative business models emerging. Companies in this field – such as charging solution provider, project management or consulting companies that are part of this project, Heliox and Over Morgen respectively – are challenged with countless possibilities and increasing complexity. How to overcome this problem? Academic research proposes a promising approach, namely the use of business model patterns for business model innovation. In short, these business model patterns are descriptions of proven practical solutions to common business model challenges. An example for a general pattern would be the business model pattern “Consumables”. It describes how to lock in a customer into an ecosystem by using a subsidized basic product and complement it with overpriced consumables. This pattern works really well and has been used by many companies (e.g. Senseo, HP, or Gillette). To support the business model innovation process of Heliox and Over Morgen as well as companies in the electric mobility space in general, we propose to systematically consolidate and develop business model patterns for the electric mobility sector and to create a database. Electric mobility patterns could not only foster creativity in the business model innovation process but also enhance collaboration in teams. By having a classified list of business model pattern for electric mobility, practitioners are equipped which a heuristic tool to create, extend and revise business models for the future.
Nature areas in North-West Europe (NWE) face an increasing number of visitors (intensified by COVID-19) resulting in an increased pressure on nature, negative environmental impacts, higher management costs, and nuisance for local residents and visitors. The high share of car use exaggerates these impacts, including peak pressures. Furthermore, the almost exclusive access by car excludes disadvantaged people, specifically those without access to a car. At the same time, the urbanised character of NWE, its dense public transport network, well-developed tourism & recreation sector, and presence of shared mobility providers offers ample opportunities for more sustainable tourism. Thus, MONA will stimulate sustainable tourism in and around nature areas in NWE which benefits nature, the environment, visitors, and the local economy. MONA will do so by encouraging a modal shift through facilitating sustainableThe pan-European Innovation Action, funded under the Horizon Europe Framework Programme, aims to promote innovative governance processes ,and help public authorities in shaping their climate mitigation and adaptation policies. To achieve this aim, the GREENGAGE project will leverage citizens’ participation and equip them with innovative digital solutions that will transform citizen’s engagement and cities’ effectiveness in delivering the European Green Deal objectives for carbon neutral cities.Focusing on mobility, air quality and healthy living, citizens will be inspired to observe and co-create their cities by sensing their urban environments. The aim to complement, validate, and enrich information in authoritative data held by the public administrations and public agencies. This will be facilitated by engaging with citizens to co-create green initiatives and to develop Citizen Observatories. In GREENGAGE, Citizen Observatories will be a place where pilot cities will co-examine environmental issues integrating novel bottom-up process with top-down perspectives. This will provide the basis to co-create and co-design innovative solutions to monitor environmental problems at ground level with the help of citizens.With two interrelated project dimensions, the project aims to enhance intelligence applied to city decision-making processes and governance by engaging with citizen observations integrated with Copernicus, GEOSS, in-situ, and socio-economic intelligence, and by delivering innovative governance models based on novel toolboxes of decision-making methodologies and technologies. The envisioned citizens observatory campaigns will be deployed and fully demonstrated in 5 pilot engagements in selected European cities and regions including: Bristol (the United Kingdom), Copenhagen (Denmark), Turano / Gerace (Italy) and the region of Noord Brabant (the Netherlands). These innovation pilots aim to highlight the need for smart city governance by promoting citizen engagement, co-creation, gathering new data which will complement existing datasets and evidence-based decision and policymaking.
