TrafficGPT: Viewing, processing and interacting with traffic foundation models
With the promotion of ChatGPT to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, large language models (LLMs) struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, the authors present TrafficGPT—a fusion of multiple LLMs and traffic foundation models. This integration yields the following key enhancements: 1) empowering LLMs with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/29485010
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Supplemental Notes:
- © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Siyao
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0000-0002-5793-378X
- Fu, Daocheng
- Liang, Wenzhe
- Zhang, Zhao
- Yu, Bin
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0000-0002-7461-625X
- Cai, Pinlong
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0000-0002-3993-0913
- Yao, Baozhen
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0000-0003-2867-2549
- Publication Date: 2024-5
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 95-105
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Serial:
- Transport Policy
- Volume: 150
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0967-070X
- Serial URL: http://www.elsevier.com/locate/issn/096707X
Subject/Index Terms
- TRT Terms: Advanced traffic management systems; Language; Traffic data; Traffic models
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01917471
- Record Type: Publication
- Files: TRIS
- Created Date: May 1 2024 9:46AM