Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm DOI Creative Commons
Sari Masri, Huthaifa I. Ashqar, Mohammed Elhenawy

et al.

Vehicles, Journal Year: 2025, Volume and Issue: 7(1), P. 11 - 11

Published: Jan. 27, 2025

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as controllers. The utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput provide feedback based on conditions in real time. LLMs centralize traditionally disconnected processes can integrate data from diverse sources context-aware decisions. also deliver tailored outputs various means such wireless signals visuals drivers, infrastructures, autonomous vehicles. To evaluate LLMs’ ability controllers, this proposed four-stage methodology. methodology includes creation environment initialization, prompt engineering, conflict identification, fine-tuning. We simulated multi-lane four-leg intersection scenarios generated detailed datasets enable detection Python simulation ground truth. used chain-of-thought prompts lead understanding the context, detecting conflicts, resolving them rules, delivering context-sensitive management solutions. evaluated performance of GPT-4o-mini, Gemini, Llama Results showed that fine-tuned GPT-mini achieved 83% accuracy an F1-score 0.84. GPT-4o-mini model exhibited promising generating actionable insights, with high ROUGE-L scores across identification 0.95, decision making 0.91, priority assignment 0.94, waiting time optimization 0.92. confirmed benefits controller real-world applications. demonstrated offer precise recommendations drivers including yielding, slowing, or stopping vehicle dynamics. demonstrates transformative potential control, enhancing efficiency safety at intersections.

Language: Английский

Investigating the role of large language models on questions about refractive surgery DOI

Süleyman Demir

International Journal of Medical Informatics, Journal Year: 2025, Volume and Issue: 195, P. 105787 - 105787

Published: Jan. 7, 2025

Language: Английский

Citations

0
Enhancing suicidal ideation detection through advanced feature selection and stacked deep learning models DOI
Shiv Shankar Prasad Shukla, Maheshwari Prasad Singh

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(4)

Published: Jan. 13, 2025

Language: Английский

Citations

0
Discovering sentiment insights: streamlining tourism review analysis with Large Language Models DOI Creative Commons
Dario Guidotti, Laura Pandolfo, Luca Pulina

et al.

Information Technology & Tourism, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

With digital technology increasingly shaping the tourism industry, understanding customer sentiment and identifying key themes in reviews is crucial for enhancing service quality. However, traditional analysis keyword extraction models typically demand significant time, computational resources, labelled data training. In this paper, we explore how Large Language Models (LLMs) can be leveraged to automatically classify as positive or negative extract relevant keywords without need dedicated Additionally, frame task a tool assist human users comprehending interpreting review content, especially scenarios where ground truth labels are unavailable. To evaluate our approach, conduct an experimental using several datasets of various LLMs. Our results demonstrate reliability LLMs zero-shot classifiers showcase efficacy approach extracting meaningful from reviews, providing valuable insights into sentiments preferences. Overall, research contributes intersection information by presenting practical solution leveraging capabilities versatile tools decision-making processes industry.

Language: Английский

Citations

0
Evaluation of Chatbots in the Emergency Management of Avulsion Injuries DOI Creative Commons
Şeyma Mustuloğlu, Büşra Pınar Deniz

Dental Traumatology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

ABSTRACT Background This study assessed the accuracy and consistency of responses provided by six Artificial Intelligence (AI) applications, ChatGPT version 3.5 (OpenAI), 4 4.0 Perplexity (Perplexity.AI), Gemini (Google), Copilot (Bing), to questions related emergency management avulsed teeth. Materials Methods Two pediatric dentists developed 18 true or false regarding dental avulsion asked public chatbots for 3 days. The were recorded compared with correct answers. SPSS program was used calculate obtained accuracies their consistency. Results achieved highest rate 95.6% over entire time frame, while (Perplexity.AI) had lowest 67.2%. (OpenAI) only AI that perfect agreement real answers, except at noon on day 1. showed weakest (6 times). Conclusions With exception ChatGPT's paid version, 4.0, do not seem ready use as main resource in managing teeth during emergencies. It might prove beneficial incorporate International Association Dental Traumatology (IADT) guidelines chatbot databases, enhancing

Language: Английский

Citations

0
Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm DOI Creative Commons
Sari Masri, Huthaifa I. Ashqar, Mohammed Elhenawy

et al.

Vehicles, Journal Year: 2025, Volume and Issue: 7(1), P. 11 - 11

Published: Jan. 27, 2025

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as controllers. The utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput provide feedback based on conditions in real time. LLMs centralize traditionally disconnected processes can integrate data from diverse sources context-aware decisions. also deliver tailored outputs various means such wireless signals visuals drivers, infrastructures, autonomous vehicles. To evaluate LLMs’ ability controllers, this proposed four-stage methodology. methodology includes creation environment initialization, prompt engineering, conflict identification, fine-tuning. We simulated multi-lane four-leg intersection scenarios generated detailed datasets enable detection Python simulation ground truth. used chain-of-thought prompts lead understanding the context, detecting conflicts, resolving them rules, delivering context-sensitive management solutions. evaluated performance of GPT-4o-mini, Gemini, Llama Results showed that fine-tuned GPT-mini achieved 83% accuracy an F1-score 0.84. GPT-4o-mini model exhibited promising generating actionable insights, with high ROUGE-L scores across identification 0.95, decision making 0.91, priority assignment 0.94, waiting time optimization 0.92. confirmed benefits controller real-world applications. demonstrated offer precise recommendations drivers including yielding, slowing, or stopping vehicle dynamics. demonstrates transformative potential control, enhancing efficiency safety at intersections.

Language: Английский

Citations

0