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Browsing by Author "Novotny, Adam"

Now showing 1 - 4 of 4
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    E-Scooter Design: Safety Measures for Next Gen Scooter
    Novotny, Adam; Mollenhauer, Michael A.; White, Elizabeth (Safe-D National UTC, 2023-05)
    Over the recent years, e-scooters have become an increasingly popular and convenient micromobility solution for short-distance trips for a wide demographic of users. Due to their accessibility, knowledge regarding proper e-scooter use and level of operating experience can vary widely. With the increase in use, there has been a rise in injuries for e-scooter riders and other road users. One possible cause is that the true performance capabilities of e-scooters vary based upon their designs; users are unaware of these differences or how to accommodate their riding behavior to retain a safe experience. This relationship between safety outcomes and e-scooter design attribute has yet to be established. Until recently, very little formal research has been conducted on the safety of this form of transportation or on the optimal design for e-scooters. Safety concerns may limit the widespread adoption of e-scooters as a legitimate transportation option. To address this concern, the Virginia Tech Transportation Institute (VTTI), in collaboration with Ford Motor Company and Spin, conducted a controlled participant study on the Virginia Smart Roads to evaluate and compare various e-scooter designs and study how rider specific factors contribute to performance and safety. The results from this study will be used to inform e-scooter companies and manufacturers on design recommendations for improved e-scooter safety.
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    E-Scooter Safety Assessment and Campus Deployment Planning
    White, Elizabeth; Mollenhauer, Michael A.; Robinson, Sarah; Novotny, Adam (Safe-D University Transportation Center, 2023-12)
    E-Scooters are a popular new service that provide last mile transportation, but there are reports of safety concerns for riders and impingement on other users of rights of way. Little formal research has been conducted on E-Scooter safety or the optimal approach to deployment to decrease nuisance issues. To address this, VTTI and Spin deployed a fleet of E-Scooters on the Virginia Tech campus through an exclusive, controlled research program. Through on-scooter data acquisition systems, fixed infrastructure cameras, anecdotal injury reports, and surveys, data was collected to assess safety impact as well as to understand beneficial and problematic user behaviors and patterns for subsequent countermeasure development and deployment recommendations. The resulting naturalistic dataset includes over 9,000 miles of riding data. Overall, the E-Scooter deployment on the Virginia Tech campus was safer than other reported deployments. The operational constraints that were put in place were largely effective, and with the additional results from this study, some additional constraints and expanded outreach programs may make future deployments even safer. The campus community largely considered the deployment of E-Scooters a clean alternative transportation option and viewed the service favorably.
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    A Holistic Approach to Reducing Adolescent Risky Behavior: Combining Driving Performance Measures with Psychological and Neurobiological Measures of Risky Adolescent Behavior
    Novotny, Adam; Noble, Alex; Kim-Spoon, Jungmeen; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-08-02)
    Adolescent drivers are one of the age groups with the highest crash risks due to factors such as inexperience and poor judgment, an increased propensity for risk-taking, and a higher likelihood to engage in secondary tasks. Previous research has indicated that there may be correlations between teen risky driving behaviors and health risk behaviors such as substance use. Therefore, it is important to understand if there is a relationship between adolescent risky behaviors and unsafe driving outcomes. To investigate this, the Virginia Tech Transportation Institute (VTTI) partnered with the Virginia Tech JK Lifespan Development Lab to conduct a pilot study. During this study, 17 novice teen drivers within 1 month of obtaining their provisional license who were also participating in the Neurobehavioral Determinants of Health-Related Behaviors (NDHRB) Study were recruited. Participants’ personal vehicles were instrumented with VTTI’s mini-data acquisition system, which collected driving performance and behavior data. Data was collected over a 6-month period and analyzed for kinematic risky driving events, eye-glance behavior, secondary task engagement, and seatbelt use. This data was combined with the psychosocial/neurobiological data collected from the surveys, questionnaires, and tests during the NDHRB study. Correlations were discovered between risky driving behaviors (kinematic risky driving events, eye-glance behaviors, secondary task engagement and cellphone use, and proper seatbelt use), and psychosocial/neurobiological measures (reported substance use, insula activation during a lottery task, general health self-assessment, Domain-Specific Risk-Taking Scale health safety risk, health risk behavior, and self-reported risk). The results from this pilot study were promising and point to the need for future research into teen risky behaviors, either driving or otherwise, to create countermeasures to reduce teen crash rates.
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    What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach
    White, Elizabeth; Guo, Feng; Han, Shu; Mollenhauer, Michael A.; Broaddus, Andrea; Sweeney, Ted; Robinson, Sarah; Novotny, Adam; Buehler, Ralph (Elsevier, 2023-06)
    Introduction: 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. Method: 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: 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. Conclusions: 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. Practical Applications: 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.