Mobility Mentoring® combineert het onderwerp armoede met de laatste inzichten vanuit de hersenwetenschap over de effecten van schaarste en armoede en de ontwikkelbaarheid van hersenfuncties. Deze nieuwe aanpak helpt mensen bij de aanpak van hun financiële en sociale problemen. Het lectoraat Schulden & Incasso van de Hogeschool Utrecht, Platform31 en Impuls ambiëren een effectievere aanpak van financiële problematiek van huishoudens en zochten naar organisaties die de inzichten uit de Schaarste-theorie op een vruchtbare manier vertalen naar hun dagelijkse praktijk.
Global climate agreements call for action and an integrated perspective on mobility, energy and overall consumption. Municipalities in dense, urban areas are challenged with facilitating this transition with limited space and energy resources, and with future uncertainties. One important aspect of the transition is the adoption of electric vehicles, which includes the adequate design of charging infrastructure. Another important goal is a modal shift in transportation. This study investigated over 80 urban mobility policy measures that are in the policy roadmap of two of the largest municipalities of the Netherlands. This analysis consists of an inventory of policy measures, an evaluation of their environmental effects and conceptualizations of the policy objectives and conditions within the mobility transitions. The findings reveal that the two municipalities have similarities in means, there is still little anticipation of future technology and policy conditions could be further satisfied by introducing tailored measures for specific user groups.
In the Interreg Smart Shared Green Mobility Hubs project, electric shared mobility is offered through eHUBs in the city. eHUBs are physical places inneighbourhoods where shared mobility is offered, with the intention of changing citizens’ travel behaviour by creating attractive alternatives to private car use.In this research, we aimed to gain insight into psychological factors that influence car owners’ intentions to try out shared electric vehicles from an eHUB in order to ascertain:1. The psychological factors that determine whether car owners are willing to try out shared electric modalities in the eHUBs and whether these factors are identical for cities with different mobility contexts.2. How these insights into psychological determinants can be applied to entice car owners to try out shared electric modalities in the eHUBs.Research was conducted in two cities: Amsterdam (the Netherlands) and Leuven (Belgium). An onlinesurvey was distributed to car owners in both cities inSeptember 2020 and, additionally, interviews wereheld with 12 car owners in each city.In general, car owners from Amsterdam and Leuven seem positive about the prospect of having eHUBs in their cities. However, they show less interest inusing the eHUBs themselves, as they are satisfied with their private car, which suits their mobility needs. Car owners mentioned the following reasons for notbeing interested in trying out the eHUBs: they simply do not see a need to do so, the costs involved with usage, the need to plan ahead, the expected hasslewith registration and ‘figuring out how it works’, having other travel needs, safety concerns, having to travel a distance to get to the vehicle, and a preferencefor ownership. Car owners who indicated that they felt neutral, or that they were likely to try out an eHUB, mentioned the following reasons for doing so:curiosity, attractive pricing, convenience, not owning a vehicle like those offered in an eHUB, environmental concerns, availability nearby, and necessity when theirown vehicle is unavailable.In both cities, the most important predictor determining car owners’ intention to try out an eHUB is the perceived usefulness of trying out an eHUB.In Amsterdam, experience with shared mobility and familiarity with the concept were the second and third factors determining car owners’ interest in tryingout shared mobility. In Leuven, pro-environmental attitude was the second factor determining car owners’ openness to trying out the eHUBs, and agewas the third factor, with older car owners being less likely to try one out.Having established that perceived usefulness was the most important determinant for car owners to try out shared electric vehicles from an eHUB, weconducted additional research, which showed that, in both cities, three factors contribute to perceived usefulness, in order of relevance: (1) injunctive norms(e.g., perceiving that society views trying out eHUBs as correct behaviour); (2) trust in shared electric mobility as a solution to problems in the city (e.g., expecting private car owners’ uptake of eHUBs to contributeto cleaner air, reduce traffic jams in city, and combat climate change); and (3) trust in the quality and safety of the vehicles, including the protection of users’privacy. In Amsterdam specifically, two additional factors contributed to perceived usefulness of eHUBs: drivers’ confidence in their capacity to try out anunfamiliar vehicle from the eHUB and experience of travelling in various modes of transport.Drawing on the relevant literature, the results of our research, and our behavioural expertise, we make the following recommendations to increase car users’ uptake of shared e-mobility:1. Address car owners’ attentional bias, which filters out messages on alternative transport modes.2. Emphasise benefits of (trying out) shared mobility from different perspectives so that multiple goals can be addressed.3. Change the environment and the infrastructure, as infrastructure determines choice of transport.4. For Leuven specifically: target younger car owners and car owners with high pro-environmental attitudes.5. For Amsterdam specifically: provide information on eHUBs and opportunities for trying out eHUBs.
MULTIFILE
Dutch Cycling Intelligence (DCI) embodies all Dutch cycling knowledge to enhances customer-oriented cycling policy. Based on the data-driven cycle policy enhancement tools and knowledge of the Breda University of Applied Sciences, DCI is the next step in creating a learning community between road authorities, consultants, cycling industry, and knowledge institutes with their students. The DCI consists of three pilars:- Connecting- Accelerating knowledge- Developing knowledgeConnecting There are many stakeholders and specialists in the cycling domain. Specialists with additional knowledge about socio-cultural impacts, geo-special knowledge, and technical traffic solutions. All of these specialists need each other to ensure a perfect balance between the (electric) bicycle, the cyclist and the cycle path in its environment. DCI connects and brings together all kind of different specialists.Accelerating knowledge Many bicycle innovations take place in so-called living labs. Within the living lab, the triple helix collaboration between road authorities the industry and knowledge institutes is key. Being actively involved in state-of-the-art innovations creates an inspiring work and learning environment for students and staff. A practical example of a successful living lab is the cycle superhighway F261 between Tilburg and Waalwijk, where BUAS tested new cycle route signage. Next, the Cycling Lab F58 is created, where the road authorities Breda and Tilburg opened up physical cycling infrastructure for entrepreneurs in the bicycle domain and knowledge institutes to develop e-cycling innovation. The living labs are test environments where pilots can be carried out in practice and an excellent environment for students to conduct scientifically applied research.Developing knowledge Ultimately, data and information must be translated into knowledge. With a team of specialists and partners Breda University of applied sciences developed knowledge and tools to monitor and evaluate cycling behavior. By participating in (inter)national research programs BUAS has become one of the frontrunners in data-driven cycle policy enhancement. In close collaboration with road authorities, knowledge institutes as well as consultants, new insights and answers are developed in an international context. By an active knowledge contribution to the network of the Dutch Cycling Embassy, BUAS aims to strengthen its position and add to the global sustainability challenges. Partners: Province Noord-Brabant, Province Utrecht, Vervoerregio Amsterdam, Dutch Cycling Embassy, Tour de Force, University of Amsterdam, Technical University Eindhoven, Technical University Delft, Utrecht University, DTV Capacity building, Dat.mobility, Goudappel Coffeng, Argaleo, Stratopo, Move.Mobility Clients:Province Noord-Brabant, Province Utrecht, Province Zuid-Holland, Tilburg, Breda, Tour de Force
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
While several governmental and research efforts are set upon mobility-as-a-service (MaaS), most of them are driven by individual travel behavior and potential usage. Scholars argue that this is a too narrow perspective when evaluating government projects because choices individuals make in a private setting might not accurately reflect their preferences towards public policy. Participatory Value Evaluation (PVE) is a novel evaluation framework specifically designed to alleviate this issue by analyzing preferences on the allocation of public budgets. Thus, based on PVE, this project aims at assessing different features of MaaS-services (e.g. enhancing mobility of the elderly and the poor, complementing public transport, etc.) from a social desirability perspective and compare them with investments in alternative social projects. Specifically, it aims at establishing the citizen value of MaaS as compared to social investments in green/recreational areas or transport infrastructure (e.g. bike or bus lanes), and eliciting trade-offs between different features of them. The project includes the selection of different investment projects (and their features) that are politically relevant in Rotterdam. It also includes a qualitative assessment on the way individuals evaluate different social projects and their features and a quantitative assessment based on choice models that allow eliciting trade-offs between different attributes and projects. Finally, policy recommendations are provided based on these results. They allow conceiving investments projects to maximize the societal benefits as well as to construct optimal investment portfolios. This information is to be used as a complement of the evaluation of projects on the basis of individual preferences.
