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185 records · Page 11

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. In conclusion, the crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

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Sierra/SolidMechanics, ITAR Users Guide (V.5.24): Addendum for Shock Capabilities

This is an addendum to the Sierra/SolidMechanics 5.24 User’s Guide that documents additional capabilities available only in alternate versions of the Sierra/SolidMechanics (Sierra/SM) code. These alternate versions are enhanced to provide capabilities that are regulated under the U.S. Department of State’s International Traffic in Arms Regulations (ITAR) export control rules. The ITAR regulated codes are only distributed to entities that comply with the ITAR export control requirements. The ITAR enhancements to Sierra/SM include material models with an energy-dependent pressure response (appropriate for very large deformations and strain rates) and capabilities for blast modeling. This document is an addendum only; the standard Sierra/SolidMechanics 5.24 User’s Guide should be referenced for most general descriptions of code capability and use.

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Autonomous Ride-Share Fleet to Reduce Traffic Congestion for Suburban Commuters

As urbanization progresses in areas across the United States, traffic is worsening as more personal vehicles are required to ferry suburban commuters between home and work. Many suburban commuters would prefer public transportation over driving a personal vehicle to use their commute time more efficiently, but existing infrastructure can make conventional public transit inconvenient or impossible. There is a need for a wide-spread system that can safely, conveniently, and cost-effectively transport suburban commuters to and from work. This will decrease congestion on roads, allowing suburban areas to continue growing in population without dramatically increasing commute times. The customer (suburban commuters) needs a system that enables them to commute to and from work more efficiently. This efficiency can be thought of as both the total time spent commuting, as well as the quality of that time spent commuting. The “quality” of the time is subjective, but in general is a metric of the ability of the commuter to focus on things other than traveling during the commute process. The new system should decrease the total time commuting, but increase the quality of the commute time. Collectively Americans spend a total of 55.2 minutes per day commuting, up from 43.4 minutes per day in 1980. Additionally, as of 2013, 76% of Americans get to work by driving alone, up from 64% in 19803. The 40-hour work-week was established in the U.S in 1940 and has not changed since. The result is that Americans spend an increasing amount of time commuting and at a lower quality, but the standard work week has not changed. This decreases the amount of time adults in the U.S have for leisure, friends, and family. A system solution has the potential to improve this, particularly with the advent of new and future technologies. This system is picked specifically for suburban commuters, as they have the highest potential to be helped.

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Synthesis of Correct Digital Controller Models from Specifications by Model Transformation (21-0320)

The design of high consequence controllers (in weapons systems, autonomy, etc.) that do what they are supposed to do is a significant challenge. Testing simply does not come close to meeting the requirements for assurance. Today circuit designers at Sandia (and elsewhere) typically capture the core behavior of their components using state models in tools such as STATEFLOW. They then check that their models meet certain requirements (e.g. “The system bus must not deadlock” or “both traffic lights at an intersection must not be green at the same time”) using tools called model checkers. If the model checker returns “yes” then the property is guaranteed to be satisfied by the model. However, there are several drawbacks to this industry practice: (1) there is a lot of detail to get right, this is particularly challenging when there are multiple components requiring complex coordination (2) any errors returned by the model checker have to be traced back through the design and fixed, necessitating rework, (3) there are severe scalability problems with this approach, particularly when dealing with concurrency. All this places high demands on the designers who now face not only an accelerated schedule but also controllers of increasing complexity. This report describes a new and fundamentally different approach to the construction of safety-critical digital controllers. Instead of directly constructing a complete model and then trying to verify it, the designer can start with an initial abstract (think “sketch”) model plus the requirements, from which a correct concrete model is automatically synthesized. There is no need for post-hoc verification of required functional properties. Having tool to carry this out will significantly impact the nation’s ability to ensure the safety of high-consequence digital systems. The approach has been implemented in a prototype tool, along with a suite of examples, including ones that reflect actual problems faced by designers. Our approach operates on a variant of Statecharts developed at Sandia called Qspecs. Statecharts are a widely used formalism for developing concurrent reactive systems, supporting scalability through allowing state models containing composite states, which are the serial or parallel composition of substates which can themselves contain statecharts. Statecharts enable an incremental style of development, in which states are progressively refined to incorporate greater detail in an incremental model of software development. Our approach formulates a set of constraints from the structure of the models and the requirements and propagates these constraints to a fixpoint. The solution to the constraints is an inductive invariant along with guards on the transitions. We also show how our approach extends to implementation refinement, decomposition, composition, and elaboration. We currently handle safety requirements written in LTL (Linear Temporal Logic)

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Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

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