Browsing by Subject "Transferability"

Now showing 1 - 4 of 4
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    Surrogate-based Transferability of Crash Modification Factors
    (2024-08) Gao, Jingru
    Crash modification factors (CMF), commonly used to measure roadway treatments’ safety effectiveness, are critical inputs to Highway Safety Manual (HSM). Although the transferability issue of CMF has been a hot topic ever since HSM first came out, most research has tended to focus on geographic transferability. Existing CMFs are statistical summaries of conditions prevailing on North American roads in the past few decades. As automated vehicles (AV) gradually increase in market share and improve in capability, an emerging issue regarding CMF’s transferability is the transfer of CMFs estimated in current situation to future situation where AVs are present. This study aimed to develop novel methods, using surrogate measures, for estimating CMF associated with roadway treatments and how existing estimates of CMFs might be transferred to situation with AVs present. When the crash-generating process can be represented by a directed acyclic graph (DAG), under certain conditions, a set of surrogates that support the transfer of a CMF estimated in one situation to a different situation can be identified based on the connectivity features of the DAG. This allows one to learn about the relation between crash outcome and surrogates in one situation, learn about the relation between surrogates and the countermeasure of interest in a different situation, and combine these relations to transport CMF from one situation to another. Analytical results were developed for three simplified but plausible examples and illustrated by computational examples to explicate this claim. Besides, asymptotic theories and uncertainty quantification methods for the resulting CMF estimators were established to support the practical application of this approach. Finally, suggestions for further study were provided. This study is an effort to answer a broader question of how past roadway safety research accomplishments might be leveraged with a newer condition, an environment with AVs, to support future decision-making.
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    Temporal transferability of models of mode-destination choice for the Greater Toronto and Hamilton Area
    (Journal of Transport and Land Use, 2014) Fox, James; Daly, Andrew; Hess, Stephane; Miller, Eric
    Transport planning relies extensively on forecasts of traveler behavior over horizons of 20 years and more. Implicit in such forecasts is the assumption that travelers’ tastes, as represented by the behavioral model parameters, are constant over time. In technical terms, this assumption is referred to as the "temporal transferability" of the models. This paper summarizes the findings from a literature review that demonstrates there is little evidence about the transferability of mode-destination models over typical forecasting horizons. The literature review shows a relative lack of empirical studies given the importance of the issue. To provide further insights and evidence, models of commuter mode-destination choice been developed from household interview data collected across the Greater Toronto and Hamilton Area in 1986, 1996, 2001, and 2006. The analysis demonstrates that improving model specification improves the transferability of the models, and in general the transferability declines as the transfer period increases. The transferability of the level-of-service parameters is higher than transferability of the cost parameters, which has important implications when considering the accuracy of forecasts for different types of policy. The transferred models over-predict the key change in mode share over the transfer period—specifically, the shift from local transit to auto driver between 1986 and 1996—but under-predict the growth in commuting tour lengths over the same period.
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    Towards Human-Like Machine Intelligence: Generalizability, Transferability, and Trustworthiness
    (2022-06) Luo, Yan
    Acquiring human-like machine intelligence is a long-standing goal of machine learning. Thanks to the availability of large-scale datasets and the GPU acceleration, modern learning methods achieve remarkable success. Although it surpasses humans on several tasks, e.g., the game of go, there is still a gap between machine intelligence and human intelligence. The reasons are two-fold. Firstly, how the human brain produces intelligence is still little-known, and how to apply the mechanisms that are discovered in the research of neuroscience to machine intelligence remains unclear. Secondly, human intelligence has been proven to be versatile to a wide variety of capacities, e.g., abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, problem-solving, etc. It is unclear how to comprehensively measure human intelligence. There is no evidence thus far that machine intelligence can be a replacement for human intelligence in a wide range of real-world applications. Instead of diving into human brain neurons, Piaget studies human intelligence from the aspect of cognitive development along the key periods of growth. In Piaget's theory, two processes are closely related to human intelligence, that is, assimilation and accommodation. Assimilation aims to fit new information into existing cognitive schemas, while accommodation aims to take new information in one's environment and alter existing schemas to fit in the new information. We focus on three specific characteristics, i.e., generalizability, transferability, and trustworthiness, that center around assimilation and accommodation. Specifically, generalizability is an important yet generic concept in machine learning. Instead, we study the generalizability that takes place in the process of fitting new information associated to unknown classes into the knowledge w.r.t. known classes. Secondly, we explore how to transfer the knowledge learned from the source domain samples to the target domain with very few target-domain examples. Last but not least, there is still a gap between state-of-the-art learning-based approaches and a perfect one. Therefore, there is a critical need to understand the trustworthiness of machine intelligence.
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    Transferability of Empirical Potentials and the Knowledgebase of Interatomic Models (KIM)
    (2016-04) Karls, Daniel
    Empirical potentials have proven to be an indispensable tool in understanding complex material behavior at the atomic scale due to their unrivaled computational efficiency. However, as they are currently used in the materials community, the realization of their full utility is stifled by a number of implementational difficulties. An emerging project specifically aimed to address these problems is the Knowledgebase of Interatomic Models (KIM). The primary purpose of KIM is to serve as an open-source, publically accessible repository of standardized implementations of empirical potentials (Models), simulation codes which use them to compute material properties (Tests), and first-principles/experimental data corresponding to these properties (Reference Data). Aside from eliminating the redundant expenditure of scientific resources and the irreproducibility of results computed using empirical potentials, a unique benefit offered by KIM is the ability to gain a further understanding of a Model's transferability, i.e. its ability to make accurate predictions for material properties which it was not fitted to reproduce. In the present work, we begin by surveying the various classes of mathematical representations of atomic environments which are used to define empirical potentials. We then proceed to offer a broad characterization of empirical potentials in the context of machine learning which reveals three distinct categories with which any potential may be associated. Combining one of the aforementioned representations of atomic environments with a suitable regression technique, we define the Regression Algorithm for Transferability Estimation (RATE), which permits a quantitative estimation of the transferability of an arbitrary potential. Finally, we demonstrate the application of RATE to a specific training set consisting of bulk structures, clusters, surfaces, and nanostructures of silicon. A specific analysis of the underlying quantities inferred by RATE which are used to characterize transferability is provided.