Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks

• DOI: 10.48550/arxiv.2405.02357
• Publication Date: 2024-05-02
• Authors: Zijian Zhang; Yujie Sun; Zepu Wang; Yuqi Nie; Xiaobo Ma; Peng Sun; Ruolin Li
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Machine Learning (cs.LG)

Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers viable solution to address conflict between increasing demands and limitations infrastructure. Predicting human travel significant aiding various urban management tasks, such as taxi dispatch planning. Machine learning deep methods are favored for their flexibility accuracy. Nowadays, with advent large language models (LLMs), many researchers have combined these previous techniques or applied LLMs directly predict future behaviors. However, there lack comprehensive studies on how can contribute this field. This survey explores existing approaches using mobility forecasting problems. We provide literature review concerning applications within systems, elucidating utilize LLMs, showcasing recent state-of-the-art advancements, identifying challenges that must be overcome fully leverage domain.