  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 mobility forecasting problems. 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.
