ChatGPT as the Transportation Equity Information Source for Scientific Writing

TL;DR

This study evaluates ChatGPT's ability to generate transportation equity abstracts, revealing moderate similarity to human abstracts and highlighting its potential as an information source with current limitations.

Contribution

It provides an empirical analysis of ChatGPT's effectiveness in producing transportation equity content compared to human-written abstracts.

Findings

Average similarity between ChatGPT and human abstracts is 58%.

High similarity abstracts share common keywords like access and policy.

Significant differences exist in thematic clustering between high and low similarity abstracts.

Abstract

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