Safety at the edge: a safety framework to identify edge conditions in the future transportation system with highly automated vehicles.

Automated driving systems (ADS) have the potential for improving safety but also pose the risk of extending the transportation system beyond its edge conditions, beyond the operating conditions (operational design domain (ODD)) under which a given ADS or feature thereof is specifically designed to function. The ODD itself is a function of the known bounds and the unknown bounds of operation. The known bounds are those defined by vehicle designers; the unknown bounds arise based on a person operating the system outside the assumptions on which the vehicle was built. The process of identifying and mitigating risk of possible failures at the edge conditions is a cornerstone of systems safety engineering (SSE); however, SSE practitioners may not always account for the assumptions on which their risk mitigation resolutions are based. This is a particularly critical issue with the algorithms developed for highly automated vehicles (HAVs). The injury prevention community, engineers and designers must recognise that automation has introduced a fundamental shift in transportation safety and requires a new paradigm for transportation epidemiology and safety science that incorporates what edge conditions exist and how they may incite failure. Towards providing a foundational organising framework for the injury prevention community to engage with HAV development, we propose a blending of two classic safety models: the Swiss Cheese Model, which is focused on safety layers and redundancy, and the Haddon Matrix, which identifies actors and their responsibilities before, during and after an event.

Spatial Differences and Costs of Emissions at U.S. Airport Hubs.

As local governments plan to expand airport infrastructure and build air service, monetized estimates of damages from air pollution are important for balancing environmental impacts. While it is well-known that aircraft emissions near airports directly affect nearby populations, it is less clear how the airport-specific aircraft operations and impacts result in monetized damages to human health and the environment. We model aircraft and ground support equipment emissions at major U.S. airports and estimate the monetized human health and environmental damages of near airport (within 60 miles) emissions. County-specific unit damage costs for PM, SOx, NOx, and VOCs and damage valuations for CO and CO2 are used along with aircraft emissions estimations at airports to determine impacts. We find that near-airport emissions at major U.S. airports caused a total of $1.9 billion in damages in 2013, with airports contributing between $720 thousand and $190 million each. These damages vary by airport from $1 to $9 per seat per one-way flight and costs per passenger are often greater than airport charges levied on airlines for infrastructure use. As the U.S. aviation system grows, it is possible to minimize human and environmental costs by shifting aircraft technologies and expanding service into airports where fewer impacts are likely to occur.

Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy.

The highest lifetime risk for a motor vehicle crash is immediately after the point of licensure, with teen drivers most at risk. Comprehensive teen driver licensing policies that require completion of driver education and behind-the-wheel training along with Graduated Driver Licensing (GDL) are associated with lower young driver crash rates early in licensure. We hypothesize that lack of financial resources and travel time to driving schools reduce the likelihood for teens to complete driver training and gain a young driver's license before age 18. We utilize licensing data from the Ohio Bureau of Motor Vehicles on over 35,000 applicants between 15.5 and 25 years old collected between 2017 and 2019. This dataset of driving schools is maintained by the Ohio Department of Public Safety and is linked with Census tract-level socioeconomic data from the U.S. Census. Using logit models, we estimate the completion of driver training and license obtainment among young drivers in the Columbus, Ohio metro area. We find that young drivers in lower-income Census tracts have a lower likelihood to complete driver training and get licensed before age 18. As travel time to driving schools increases, teens in wealthier Census tracts are more likely to forgo driver training and licensure than teens in lower-income Census tracts. For jurisdictions aspiring to improve safe driving for young drivers, our findings help shape recommendations on policies to enhance access to driver training and licensure especially among teens living in lower-income Census tracts.

Evaluating cyclist biometrics to develop urban transportation safety metrics.

The transportation safety paradigm for urban transportation - particularly safety for those walking and cycling - relies on counting crashes to parameterize safety. These objective measures of safety are spatially static and reflective of past events: they can be enriched by including the human response to risk at diverse infrastructure designs. This perceived risk has been well captured qualitatively in the transportation safety literature; in the following study, we seek to develop a quantitative methodology that captures perceived risk as a continuous measure of human biometrics. Building on diverse safety-critical fields, we hypothesize that the perception of safety can be measured proactively with traveler biometrics, including eye and head movements, such that high readings of biometric indicators correlate with less safe areas. We collect biometric data from cyclists traversing an urban corridor with a protected, yet not continuously, cycle lane. By isolating and correlating peaks in cyclist biometric measures with infrastructure design, we develop a set of continuous variables - lateral head movements, gaze velocity, and off-mean gaze distance, both independently and as a vector - that allow for the evaluation of urban infrastructure based on perceived risk. The results reflect that higher biometric readings correspond to less safe (i.e., unprotected) areas, indicating that perceived risk can be measured proactively with biometric data.