Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1561444

Mape Maker: A Scenario Creator

Goujard, Guillaume·Watson, Jean-Paul·Woodruff, David L.
View original source ↗Read source document ↗Export metadata ↓

Abstract

We describe algorithms for creating probabilistic scenarios for the situation when the underlying forecast methodology is modeled as being more (or less) accurate than it has been historically. Such scenarios can be used in studies that extend into the future and may need to consider the possibility that forecast technology will improve. Our approach can also be used to generate alternative realizations of renewable energy production that are consistent with historical forecast accuracy, in effect serving as a method for creating families of realistic alternatives — which are often critical in simulation-based analysis methodologies.

Explore related subjects

42 ENGINEERING97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Goujard, Guillaume, Watson, Jean-Paul, Woodruff, David L.. 2019-09-05. Mape Maker: A Scenario Creator. https://doi.org/10.2172/1561444

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗