ChatGPT as the Transportation Equity Information Source for Scientific Writing

Transportation equity is an interdisciplinary agenda that requires both transportation and social inputs. Traditionally, transportation equity information are sources from public libraries, conferences, televisions, social media, among other. Artificial intelligence (AI) tools including advanced language models such as ChatGPT are becoming favorite information sources. However, their credibility has not been well explored. This study explored the content and usefulness of ChatGPT-generated information related to transportation equity. It utilized 152 papers retrieved through the Web of Science (WoS) repository. The prompt was crafted for ChatGPT to provide an abstract given the title of the paper. The ChatGPT-based abstracts were then compared to human-written abstracts using statistical tools and unsupervised text mining. The results indicate that a weak similarity between ChatGPT and human-written abstracts. On average, the human-written abstracts and ChatGPT generated abstracts were about 58% similar, with a maximum and minimum of 97% and 1.4%, respectively. The keywords from the abstracts of papers with over the mean similarity score were more likely to be similar whereas those from below the average score were less likely to be similar. Themes with high similarity scores include access, public transit, and policy, among others. Further, clear differences in the key pattern of clusters for high and low similarity score abstracts was observed. Contrarily, the findings from collocated keywords were inconclusive. The study findings suggest that ChatGPT has the potential to be a source of transportation equity information. However, currently, a great amount of attention is needed before a user can utilize materials from ChatGPT

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, such as fluid mechanics, solid mechanics, materials science, etc. Neural networks, in particular, play a central role in this hybridization. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse; a scenario that is true in many scientific fields. Nonetheless, neural networks offer a strong foundation to digest physical-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce underlying physics: (i) physics-guided neural networks (PgNN), (ii) physics-informed neural networks (PiNN) and (iii) physics-encoded neural networks (PeNN). These approaches offer unique advantages to accelerate the modeling of complex multiscale multi-physics phenomena. They also come with unique drawbacks and suffer from unresolved limitations (e.g., stability, convergence, and generalization) that call for further research. This study aims to present an in-depth review of the three neural network frameworks (i.e., PgNN, PiNN, and PeNN) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed; limitations are discussed; and future research opportunities in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers are presented. This critical review provides a solid starting point for researchers and engineers to comprehend how to integrate different layers of physics into neural networks.

Patterns of near-crash events in a naturalistic driving dataset: applying rules mining

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.