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BACKGROUND: Road crashes continue to pose a significant threat to global health. Young drivers aged between 18 and 25 are over-represented in road injury and fatality statistics, especially the first six months after obtaining their license. This study is the first multi-centre two-arm parallel-group individually randomised controlled trial (the FEEDBACK Trial) that will examine whether the delivery of personalised driver feedback plus financial incentives is superior to no feedback and no financial incentives in reducing motor vehicle crashes among young drivers (18 to 20 years) during the first year of provisional licensing. METHODS: A total of 3,610 young drivers on their provisional licence (P1, the first-year provisional licensing) will participate in the trial over 28 weeks, including a 4-week baseline, 20-week intervention and 4-week post-intervention period. The primary outcome of the study will be police-reported crashes over the 20-week intervention period and the 4-week post-intervention period. Secondary outcomes include driving behaviours such as speeding and harsh braking that contribute to road crashes, which will be attained weekly from mobile telematics delivered to a smartphone app. DISCUSSION: Assuming a positive finding associated with personalised driver feedback and financial incentives in reducing road crashes among young drivers, the study will provide important evidence to support policymakers in introducing the intervention(s) as a key strategy to mitigate the risks associated with the burden of road injury among this vulnerable population. TRIAL REGISTRATION: Registered under the Australian New Zealand Clinical Trials Registry (ANZCTR) - ACTRN12623000387628p on April 17, 2023.

PURPOSE: Young learner drivers commonly must record substantial supervised practice driving before independent licensure. Supervisory driver requirements can be limited or highly regulated, yet research is lacking on the effectiveness of different approaches. The current objective was to explore whether young drivers who were mostly supervised by someone who they perceived had traffic offences versus no offences had different crash records over a period of 13 years postlicensing. METHODS: DRIVE is an Australian prospective cohort study of more than 20,000 drivers who were aged 17-24 years and newly licensed during 2003-2004. They completed detailed baseline questionnaires, including whether the person they identified as supervising their learner driving the most had perceived traffic offences in the past 12 months. Responses were linked to their state crash, hospitalization, and death records to 2016. A parametric survival model was created to calculate hazard ratios of time to crash for those reporting that their supervisor had 0 versus 1 and 0 versus 2+ perceived offences, adjusting for the participants' prior crash history and other covariates. RESULTS: After adjusting for covariates, 369 participants reporting supervisory drivers with 2+ perceived offences, compared to 15,451 participants reporting no such offences, had up to 1.67 (95% confidence interval 1.10-2.53 at 6 months) times the rate of any crash for the first 2.5 years and up to 2.01 (95% confidence interval 1.26-3.19 at 3.5 years) times the rate of crashes resulting in injury for 5.5 years. DISCUSSION: Although overall supervision by a driver with two or more perceived offences was low, further attention is needed to ensure improved supervised driving experiences, with mentoring programs and professional instructor partnerships worthy of exploration.

Footpath mapping, modeling, and analysis can provide important geospatial insights to many fields of study, including transport, health, environment and urban planning. The availability of robust Geographic Information System (GIS) layers can benefit the management of infrastructure inventories, especially at local government level with urban planners responsible for the deployment and maintenance of such infrastructure. However, many cities still lack real-time information on the location, connectivity, and width of footpaths, and/or employ costly and manual survey means to gather this information. This work designs and implements an automatic pipeline for generating footpath networks based on remote sensing images using machine learning models. The annotation of segmentation tasks, especially labeling remote sensing images with specialized requirements, is very expensive, so we aim to introduce a pipeline requiring less labeled data. Considering supervised methods require large amounts of training data, we use a self-supervised method for feature representation learning to reduce annotation requirements. Then the pre-trained model is used as the encoder of the U-Net for footpath segmentation. Based on the generated masks, the footpath polygons are extracted and converted to footpath networks which can be loaded and visualized by geographic information systems conveniently. Validation results indicate considerable consistency when compared to manually collected GIS layers. The footpath network generation pipeline proposed in this work is low-cost and extensible, and it can be applied where remote sensing images are available. Github: https://github.com/WennyXY/FootpathSeg.

Extensive research has been conducted exploring associations between built environment characteristics and biking. However, these approaches have often lacked the ability to understand the interactions of the built environment, population and bicycle ridership. To overcome these limitations, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods. We conducted a retrospective analysis of travel surveys, bicycle infrastructure and population and land use characteristics in the Greater Melbourne region, Australia. To develop the urban biking typology, we used a k-medoids clustering method. Analyses revealed 5 clusters. We highlight areas with high bicycle network density and a high proportion of trips made by bike (Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and areas with high off-road and on-road bicycle network length, but a low proportion of trips made by bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips). Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, the bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further research is required to understand the generalisability of these findings to other settings.

Background Given that the majority of the world’s population live in cities, it is essential to global health efforts that we design them in ways that both reduce non-communicable diseases (NCDs) risk and that facilitate adoption and maintenance of healthy lifestyles. Current approaches tend to focus on the relationship between urban design-related factors that affect health at the local or neighbourhood level but few studies have explored this relationship both within and across entire cities, nor explored the causal pathways between urban-designed related factors and NCDs. The aim of this research program is to use computer vision, causal inference, and public health modelling methods for understanding the causal relationship between urban design and health at the neighbourhood level, and to explore intervention approaches at the city scale. Methods Phase 1 will use machine learning and computer vision techniques to analyse gridded, local-level aerial images (with an optical resolution of <20cm), of all UK and Australian cities with populations over 100,000. It will identify a variety of urban features within these images and derive associations between them and NCD incidence and risk factors identified through location-based health surveys. Phase 2, using data from prospective health cohorts and linked objective built environment data, will apply Bayesian networks to investigate the possible causal pathways between built environment, lifestyle factors, and NCD incidence. Phase 3 will estimate the health impacts of actionable changes in urban design. Using health impact assessment modelling, we will calculate the NCD burden that could be prevented if cities were to adopt urban features of healthier counterparts. A similar approach will be applied on finer-grained scale within all case study cities, enabling assessment of health impacts of changes in individual locations. Phase 4 will develop an interactive web-based toolkit to enable urban designers, p

We demonstrate a significantly simplified experimental approach for investigating liquid metallic hydrogen, which is crucial to understand the internal structure and evolution of giant planets. Plastic samples were shockcompressed and then probed by short pulses of X-rays generated by free electron lasers. By comparison with ab initio simulations, we provide indirect evidence for the creation of elemental hydrogen in shock-compressed plastics at ∼150GPa and ∼5,000K and thus in a regime where hydrogen is predicted to be metallic. Being the most common form of condensed matter in our solar system, and ostensibly the simplest of all elements, hydrogen is the model case for many theoretical studies and we provide a new possibility to benchmark models for conditions with extreme pressures and temperatures. Moreover, this approach will also allow to probe the chemical behavior of metallic hydrogen in mixture with other elements, which, besides its importance for planetary physics, may open up promising pathways for the synthesis of new materials.

The insulator–metal transition in liquid hydrogen is an important phenomenon to understand the interiors of gas giants, such as Jupiter and Saturn, as well as the physical and chemical behavior of materials at high pressures and temperatures. Here, the path toward an experimental approach is detailed based on spectrally resolved x-ray scattering, tailored to observe and characterize hydrogen metallization in dynamically compressed hydrocarbons in the regime of carbon–hydrogen phase separation. With the help of time-dependent density functional theory calculations and scattering spectra from undriven carbon samples collected at the European x-ray Free-Electron Laser Facility (EuXFEL), we demonstrate sufficient data quality for observing C–H demixing and investigating the presence of liquid metallic hydrogen in future experiments using the reprated drive laser systems at EuXFEL.