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Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents

Car use: a matter of dependency or choice? The case of commuting in Noord-Brabant.

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.

AI in transport en logistiek: dreiging of kans voor transformatie?

Patient factors that influence clinicians' decision making in self-management support: a clinical vignette study

Background and aim Self-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current decision making of health professionals can support future tailoring of self-management interventions. The aim of this study is to identify the relative importance of patient factors in health professionals’ decision making regarding self-management support. Method A factorial survey was presented to primary care physicians and nurses. The survey consisted of clinical vignettes (case descriptions), in which 11 patient factors were systematically varied. Each care provider received a set of 12 vignettes. For each vignette, they decided whether they would give this patient self-management support and whether they expected this support to be successful. The associations between respondent decisions and patient factors were explored using ordered logit regression. Results The survey was completed by 60 general practitioners and 80 nurses. Self-management support was unlikely to be provided in a third of the vignettes. The most important patient factor in the decision to provide self-management support as well as in the expectation that self-management support would be successful was motivation, followed by patient-provider relationship and illness perception. Other factors, such as depression or anxiety, education level, self-efficacy and social support, had a small impact on decisions. Disease, disease severity, knowledge of disease, and age were relatively unimportant factors. Conclusion This is the first study to explore the relative importance of patient factors in decision making and the expectations regarding the provision of self-management support to chronic disease patients. By far, the most important factor considered was patient’s motivation; unmotivated patients were less likely to receive self-management support. Future tailored interventions should incorporate strategies to enhance motivation in unmotivated patients. Furthermore, care providers should be better equipped to promote motivational change in their patients.

Modeling Electric Vehicle Charging Demand using Discrete Choice Models

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model and sociodemographic statistics of neighborhoods.

Validation of the short assessment of health literacy (SAHL-D) and short-form development: Rasch analysis

Background: Accurate measurement of health literacy is essential to improve accessibility and effectiveness of health care and prevention. One measure frequently applied in international research is the Short Assessment of Health Literacy (SAHL). While the Dutch SAHL (SAHL-D) has proven to be valid and reliable, its administration is time consuming and burdensome for participants. Our aim was to further validate, strengthen and shorten the SAHL-D using Rasch analysis. Methods: Available cross-sectional SAHL-D data was used from adult samples (N = 1231) to assess unidimensionality, local independence, item fit, person fit, item hierarchy, scale targeting, precision (person reliability and person separation), and presence of differential item functioning (DIF) depending on age, gender, education and study sample. Results: Thirteen items for a short form were selected based on item fit and DIF, and scale properties were compared between the two forms. The long form had several items with DIF for age, gender, educational level and study sample. Both forms showed lower measurement precision at higher health literacy levels. Conclusions: The findings support the validity and reliability of the SAHL-D for the long form and the short form, which can be used for a rapid assessment of health literacy in research and clinical practice.