Car use in the sprawled urban region of Noord‐Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High‐Tech Campus (2018) and the TU/e‐campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car‐dependent. Of the non‐car‐dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provi-sion) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.
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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.
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This paper presents the results of an experimental field study, in which the effects were studied of personalized travel feedback on car owners’ car habits, awareness of the environmental impact of their travel choices, and the intention to switch modes. For a period of six weeks, 349 car owners living in Amsterdam used a smart mobility app that automatically registered all their travel movements. Participants in the experiment group received information about travel distance, time, and CO2 emission. Results show that the feedback did not influence self-reported car habits, intention, and awareness, suggesting that personalized feedback may not be a one-size-fits-all solution to change travel habits.
DOCUMENT
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
The consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
Possibly, the aviation sector’s decarbonization challenge (see Dutch knowledge key in international climate study for tourism | CELTH) has profound implications for the ability of aviation-de-pendent outbound tour operators to attract capital and with that their ability to maintain or trans-form their current business portfolio (understood here as the current product offers and approximate carbon footprints, business models, and ownership structures present in this economic do-main). Knowledge about these (possible) investment risks and their business and policy implications is lacking. This project therefore addresses this knowledge gap by means of the following research questions.1. What is the current business portfolio of Dutch outbound tour operators?a. To what extend do Dutch outbound tour operators depend on aviation in terms of product offer and turnover?b. What is the relative carbon footprint share of aviation-based products compared to the total outbound product offer and turnover of Dutch outbound tour operators?2. What are investment risks of this business portfolio as indicated by investors?a. How do investors evaluate investment risks in relation to climate change mitigation and de-carbonisation?b. What are investment risks of the business portfolio of Dutch outbound tour operators?c. What are the reflections on and implications of these investment risks from the perspective of policymakers and tour operators?
