Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management

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Z. Cui, M. Zhu, S. Wang, P. Wang, Y. Zhou, Q. Cao, C. Kopca, and Y. Wang, “Traffic performance score for measuring the impact of covid-19 on urban mobility,” 2020

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Z. Cai, X. Li, B. Hui, M. Yang, B. Li, B. Li, Z. Cao, W. Li, F. Huang, and L. Si, “Star: Sql guided pre-training for context-dependent text-to-sql parsing,” arXiv preprint arXiv:2210.11888

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2022

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Publisher: ACM New York, NY

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M. Fraiwan and N. Khasawneh, “A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions,” arXiv preprint arXiv:2305.00237

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2023

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S. Lai, Z. Xu, W. Zhang, H. Liu, and H. Xiong, “Large language models as traffic signal control agents: Capacity and opportunity,” arXiv preprint arXiv:2312.16044

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2023

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L. Wang, H. Jiang, P. Cai, D. Fu, T. Wang, Z. Cui, Y. Ren, H. Yu, X. Wang, and Y. Wang, “AccidentGPT: Accident analysis and prevention from V2X Environmental Perception with Multi-modal Large Model,” arXiv preprint arXiv:2312.13156

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M. Villarreal, B. Poudel, and W. Li, “Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning,” arXiv preprint arXiv:2306.08094

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2023

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C. Sun, Y. Li, H. Li, and S. Hong, “TEST: Text prototype aligned embedding to activate LLM’s ability for time series,” arXiv preprint arXiv:2308.08241

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K. Rasul, A. Ashok, A. R. Williams, A. Khorasani, G. Adamopoulos, R. Bhagwatkar, M. Biloš, H. Ghonia, N. V. Hassen, and A. Schneider, “Lag-llama: Towards foundation models for time series forecasting,” arXiv preprint arXiv:2310.08278

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2023

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M. Jin, S. Wang, L. Ma, Z. Chu, J. Y. Zhang, X. Shi, P.-Y. Chen, Y. Liang, Y.-F. Li, and S. Pan, “Time-llm: Time series forecasting by reprogramming large language models,” arXiv preprint arXiv:2310.01728

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2024

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S. Zhang, D. Fu, W. Liang, Z. Zhang, B. Yu, P. Cai, and B. Yao, “TrafficGPT: Viewing, processing and interacting with traffic foundation models,” Transport Policy

2024

Closest in time.

Y. Ren, Y. Chen, S. Liu, B. Wang, H. Yu, and Z. Cui, “TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models,” arXiv preprint arXiv:2403.02221

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2024

Closest in time.

C. Liu, S. Yang, Q. Xu, Z. Li, C. Long, Z. Li, and R. Zhao, “Spatial-temporal large language model for traffic prediction,” arXiv preprint arXiv:2401.10134

Original