Natural language processing (NLP) is a key component of intelligent transportation systems (ITS), but it faces many challenges in the transportation domain, such as domain-specific knowledge and data, and multi-modal inputs and outputs. This paper presents TransGPT, a novel (multi-modal) large language model for the transportation domain, which consists of two independent variants: TransGPT-SM for single-modal data and TransGPT-MM for multi-modal data. TransGPT-SM is finetuned on a single-modal Transportation dataset (STD) that contains textual data from various sources in the transportation domain. TransGPT-MM is finetuned on a multi-modal Transportation dataset (MTD) that we manually collected from three areas of the transportation domain: driving tests, traffic signs, and landmarks. We evaluate TransGPT on several benchmark datasets for different tasks in the transportation domain, and show that it outperforms baseline models on most tasks. We also showcase the potential applications of TransGPT for traffic analysis and modeling, such as generating synthetic traffic scenarios, explaining traffic phenomena, answering traffic-related questions, providing traffic recommendations, and generating traffic reports. This work advances the state-of-the-art of NLP in the transportation domain and provides a useful tool for ITS researchers and practitioners.

翻译：摘要: 自然语言处理（NLP）是智能交通系统（ITS）的关键组成部分，但在交通领域面临诸多挑战，例如领域特定知识与数据、多模态输入与输出等。本文提出TransGPT——一种面向交通领域的新型（多模态）大语言模型，由两个独立变体组成：用于单模态数据的TransGPT-SM和用于多模态数据的TransGPT-MM。TransGPT-SM在单模态交通数据集（STD）上进行微调，该数据集包含交通领域多种来源的文本数据。TransGPT-MM在多模态交通数据集（MTD）上进行微调，该数据集包含我们从驾驶考试、交通标志和地标三个交通领域手动收集的数据。我们在交通领域不同任务的多个基准数据集上评估了TransGPT，结果表明其在大多数任务上优于基线模型。此外，我们还展示了TransGPT在交通分析与建模中的潜在应用，例如生成合成交通场景、解释交通现象、回答交通相关问题、提供交通建议以及生成交通报告。本工作推动了交通领域自然语言处理的最新技术水平，并为ITS研究人员和实践者提供了实用工具。