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

2024-05-03

AI Summary

This paper addresses the potential and challenges of leveraging large language models (LLMs) for traffic mobility analysis—specifically time-series forecasting. Methodologically, it presents a systematic survey and framework development: (1) it comprehensively maps the research landscape of LLMs in traffic forecasting, clarifying integration paradigms with conventional time-series models, data adaptation bottlenecks, and domain-knowledge injection mechanisms; (2) it proposes the first taxonomy of LLM-based methods for traffic mobility prediction, covering multi-source data encoding, prompt engineering, and model-cooperative modeling. Key limitations identified include inadequate semantic-temporal alignment, limited real-time inference capability, and weak interpretability. The work further outlines scalable technical pathways to overcome these constraints. Collectively, the study establishes a theoretical foundation and practical roadmap for deeply integrating LLMs into intelligent traffic forecasting systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• LLMs for traffic forecasting
• Mobility analysis in transportation
• Forecasting human travel behaviors

Innovation

• LLMs for traffic forecasting
• LLMs in mobility analysis
• LLMs for urban management