Seeing through eyes of Drivers: Space consideration in investigating visibility of Vulnerable road users involved in crashes from Driver’s perspective

Kutela, Boniphace; Oscar, Clement; Kidando, Emmanuel; Mihayo, Meshack

doi:10.1016/j.aap.2023.107260

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Comparative analysis is performed using various metrics, with this study utilizing document and collocated frequency to compare the introductions generated by humans and ChatGPT. Document frequency indicates the number of documents that contain a particular keyword, while keyword frequency measures how many times the keyword appears in a document Kutela, Oscar, Kidando, et al, 2023). Collocation frequency determines the number of times keywords appear next to each other and provides a deeper insight into the relationships between keywords.…”

Section: Figure 1 a Skeleton Of The Text Networkmentioning

confidence: 99%

Uncovering the influence of ChatGPT's prompts on traffic safety information using text mining approach

2023

DSI

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ChatGPT has emerged as a promising advanced large language model that needs prompt to gain information. However, designing a good prompt is not an easy task for many end-users. Therefore, this study intends to determine the amount of information gained because of varied amounts of information in the prompt. This study used two types of prompts, initial and improved, to query the introduction sections of 327 highly cited articles on traffic safety. The queried introduction sections were then matched with the corresponding human-written introduction sections from the same articles. Similarity tests and text network analysis were used to understand the level of similarities and the content of ChatGPT-generated and human-written introductions. The findings indicate the improved prompts, which have the addition of generic persona and information about the citations and references, changed the ChatGPT's output insignificantly. While the perfect similar contents are supposed to have a 1.0 similarity score, the initial and improved prompt's introduction materials have average similarity scores of 0.56 and 0.54, respectively. Further, the content analysis revealed that themes such as statistics, trends, safety measures, and safety technologies are more likely to have high similarity scores, irrespective of the amount of information provided in the prompt. On the other hand, themes such as human behavior, policy and regulations, public perception, and emerging technologies require a detailed level of information in their prompt to produce materials that are close to human-written materials. The prompt engineers can use the findings to evaluate their outputs and improve their prompting skills.

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Section: Figure 1 a Skeleton Of The Text Networkmentioning

confidence: 99%

Uncovering the influence of ChatGPT's prompts on traffic safety information using text mining approach

2023

DSI

View full text Add to dashboard Cite

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Prediction of sensor damage in automated vehicle involved collisions using parametric and non-parametric approaches

Kutela,

Shita,

Mbuya

et al. 2024

International Journal of Crashworthiness

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Seeing through eyes of Drivers: Space consideration in investigating visibility of Vulnerable road users involved in crashes from Driver’s perspective

Cited by 2 publications

References 28 publications

Uncovering the influence of ChatGPT's prompts on traffic safety information using text mining approach

Uncovering the influence of ChatGPT's prompts on traffic safety information using text mining approach

Prediction of sensor damage in automated vehicle involved collisions using parametric and non-parametric approaches

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