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, la...

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Bibliographic Details
Published in:Transport policy Vol. 150; pp. 95 - 105
Main Authors: Zhang, Siyao, Fu, Daocheng, Liang, Wenzhe, Zhang, Zhao, Yu, Bin, Cai, Pinlong, Yao, Baozhen
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2024
Subjects:
ITS
LLM
Supported decision-making
Traffic management
Supported decision-making
ITS
LLM
Traffic management
ISSN:0967-070X, 1879-310X
Online Access:Get full text
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Summary: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, we 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. •We propose the TrafficGPT framework, which combines large language models with expertise in traffic management.•TrafficGPT empowers LLMs to observe, analyze, process traffic data, and offer insightful decisions for traffic management.•We compare the proposed model with ChatGPT-4 Data Analyst to highlight its response accuracy and efficiency.•The adaptability and stability of TrafficGPT across various tasks, utilizing different foundational LLMs, are validated.
ISSN:0967-070X
1879-310X
DOI:10.1016/j.tranpol.2024.03.006