BACKGROUND: Recent advancements in and the proliferation of autonomous mobility technology, such as intelligent wheelchairs, have made it possible to provide mobility services for patients with reduced mobility due to musculoskeletal disorders. In the present study, we conducted a preliminary clinical study to assess the safety and feasibility of in-hospital autonomous transportation using a driverless mobility (wheelchair) for patients with musculoskeletal disorders.
METHODS: From January to February 2022, 51 patients with musculoskeletal disorders exhibiting gait disturbance who presented to our institution were included in the present study. Driverless mobility rides were conducted over a straight-line distance of 100 m from the orthopaedic outpatient reception to the payment counter after the outpatient consultation. We assessed the quality of life using an EQ-5D-5 L index and pain using a VAS score before riding the mobility to investigate the patient\'s condition. After the ride, a questionnaire survey was conducted to assess patient satisfaction on a 5-point scale. In addition, adverse events during the mobility ride were investigated.
RESULTS: Overall satisfaction levels showed that 44 out of 51 (86%) patients rated the level as 3 or higher. There were no significant differences in the level of satisfaction based on the cause of disorders or EQ-5D-5 L Index. Among 19 patients who rated the level of satisfaction as 2-3, the ratio of postoperative patients and those with pain tended to be higher (p < 0.05). While 26 of 51 (51%) patients reported moments of feeling unsafe during the mobility ride, no actual adverse events, such as collisions, were observed.
CONCLUSIONS: An in-hospital autonomous transportation service using a driverless mobility for patients with musculoskeletal disorders demonstrated high satisfaction levels and was safe with no severe adverse events observed. The expansion of autonomous mobility deployment is expected to achieve mobility as a service in medical care.

UNASSIGNED: We present a sketch for a nationwide \"Mobility Service Guarantee\" (MSG) for Austria. The approach follows a new paradigm, turning away from car-centric transport policy and planning and towards the extensive provision of public transport. The approach is complemented by the provision of demand-responsive transport services and the support of vehicle sharing as well as active mobility. This combination should serve as an effective alternative option to the use of private cars.
UNASSIGNED: The aim of this study is to develop a tangible definition of a nationwide MSG in the Austrian context as well as possible design scenarios.
UNASSIGNED: We took a multi-dimensional approach, using literature review to research existing concepts of mobility guarantees, analysing secondary data on current mobility behaviour and public transport provision and conducting stakeholder workshops.
UNASSIGNED: We define possible scenarios envisioning a nationwide MSG including different extents of maximum distance to public transport stops and minimum frequency. We discuss the limitations of the MSG with respect to spatial, temporal and modal coverage, as well as how such a guarantee could be embedded in the existing legal system.
UNASSIGNED: We conclude that a nationwide MSG could be an important element of sustainable transport policy that should be embedded in a wider strategy of infrastructure and service design as well as demand management.

Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors.
A fleet of 200 e-scooters was deployed on Virginia Tech\'s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios.
Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech\'s pedestrian-dense campus environment.
To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders.
The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.