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Speeding is one of the leading risk factors in road safety. Not only is it one of the leading causes of accidents, but it also has an extensive effect on the impact and consequences of accidents. This is especially the case for trucks, where the enforced speed limit is often dependent on local legislation and context rather than speed limit traffic signs. This study is part of the greater i-DREAMS project and aims to explore the effectiveness of an intelligent speed assistance system for truck drivers on different road types. To achieve this, a simulator experiment was performed with 34 professional truck drivers in Belgium. Participants first made a baseline drive, followed by two more drives, where they received visual information about the enforced speed limit but also visual and auditory warnings when exceeding the speed limit. The drives included different road environments with different speed limits. The results reveal a significant reduction in relevant parameters (i.e., average speed, minimum speed, maximum speed, and percentage of distance above the speed limit) when drivers received information and warnings about speeding while driving on a rural 1 × 1 road with a speed limit of 70 km/h (60 km/h for trucks). Further research is needed to validate this effect on other road types and under more-challenging conditions.

Community participation and the formation of social networks are crucial for a qualitative life. To this end, transportation plays an essential role. Many autistic people rely on public transportation for their mobility needs. However, research shows that it is not always easy for them to use it. The issues they face when using public bus transport have not yet been thoroughly studied. The current case study in Flanders aimed to give autistic people the opportunity to express the issues they face while using public bus transportation. A qualitative hermeneutic phenomenological study was carried out. Semistructured interviews were conducted with 17 autistic individuals. The interviews were analyzed based on the interpretative phenomenological analysis method. Three main themes emerged: creating predictability, limiting stimuli, and open and accessible communication. In addition, various coping strategies were described, such as the use of noise-canceling headphones. The results of this study may lead to a more autism-friendly public transportation environment. Lay Abstract Transportation plays an essential role in daily life, allowing people to participate in the community and form social relationships. Many autistic people rely on public transportation to meet their mobility needs. However, research shows that it is not always easy for them to use it. The exact issues autistic individuals face when traveling with public transportation and how public transportation can be made more autism-friendly have yet to be researched. The current study allowed autistic individuals to express themselves regarding issues they face while traveling by public bus transportation, to raise awareness for making public transportation more autism-friendly. We interviewed 17 autistic individuals about their experiences riding the bus. Three main themes emerged from the results: creating predictability, limiting stimuli, and open and accessible communication. If transport companies take initiatives related to these themes, autistic people traveling by bus can have a more pleasant experience. Participants also described coping strategies for stressful or uncomfortable situations while using public bus transportation, such as using noise-cancelling headphones or digital applications for real-time route tracking, etc. These findings may lead to a more autism-friendly public transportation.

Technological developments can optimize therapy for depression. However, early client or user involvement is crucial. The smartphone application and dashboard ‘plaTfOrm using evidence-based inTervEntions for (Mental) health’ (TOTEM), based on cognitive behavioral therapy and behavioral activation, is being developed together with clients from the start. Objective monitoring (e.g., activity/travel-related behavior) and human-in-the-loop AI machine learning allow tailored blended care, combining face-to-face therapy with online modules and Just-in-Time Adaptive Interventions. As a first co-creation step, clients with (prior) depression or depressive complaints and psychologists evaluated the usefulness of an existing Health for Travel Behaviour (HTB) application and feedback report developed for cardio patients, which monitors and improves travel-related physical activity. Online semi-structured interviews followed an HTB demonstration. In total, 16 interviews (14 clients and 2 psychologists) were transcribed and analyzed. Participants perceived the application as user-friendly, relevant, useful, attractive, and a supplement to standard care. It encourages people to engage in activities. The feedback report was also perceived as transparent, useful, and relevant. Emotional aspects are underemphasized (e.g., assessment of feelings and mental health-related psycho-education). When tailored to depression (with attention for different recovery phases), monitoring and improving travel-related physical activity was considered helpful in supplementing standard care for depression.