TransGPT: Multi-modal Generative Pre-trained Transformer for
  Transportation
Peng Wang Xiang Wei Fangxu Hu Wenjuan Han
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.