Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
It provides a literature review for researchers in transportation systems, but it is incremental as it synthesizes existing work without introducing new methods or results.
This survey addresses the lack of comprehensive studies on large language models (LLMs) for mobility analysis in transportation systems by reviewing existing approaches for time series forecasting tasks, such as predicting traffic information and human travel behaviors.
Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers a viable solution to address the conflict between increasing transportation demands and the limitations of transportation infrastructure. Predicting human travel is significant in aiding various transportation and urban management tasks, such as taxi dispatch and urban planning. Machine learning and deep learning methods are favored for their flexibility and accuracy. Nowadays, with the advent of large language models (LLMs), many researchers have combined these models with previous techniques or applied LLMs to directly predict future traffic information and human travel behaviors. However, there is a lack of comprehensive studies on how LLMs can contribute to this field. This survey explores existing approaches using LLMs for time series forecasting problems for mobility in transportation systems. We provide a literature review concerning the forecasting applications within transportation systems, elucidating how researchers utilize LLMs, showcasing recent state-of-the-art advancements, and identifying the challenges that must be overcome to fully leverage LLMs in this domain.