Traffic forecasting is crucial for intelligent transportation systems.It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data.However,recent deep-l...Traffic forecasting is crucial for intelligent transportation systems.It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data.However,recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results.Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models.To tackle these challenges,we propose a traffic flow prediction model based on large language models(LLMs)to generate explainable traffic predictions,named xTP-LLM.By transferring multi-modal traffic data into natural language descriptions,xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data.The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data.Empirically,xTP-LLM shows competitive accuracy compared with deep learning baselines,while providing an intuitive and reliable explanation for predictions.This study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.展开更多
基金National Natural Science Foundation of China(No.52302379)Guangdong Provincial Natural Science Foundation-General Project(No.2024A1515011790)+3 种基金Guangzhou Basic and Applied Basic Research Projects(Nos.2023A03J0106 and 2024A04J4290)Guangdong Province General Universities Youth Innovative Talents Project(No.2023KQNCX100)Guangzhou Municipal Science and Technology Project(No.2023A03J0011)Nansha District Key R&D Project(No.2023ZD006).
文摘Traffic forecasting is crucial for intelligent transportation systems.It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data.However,recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results.Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models.To tackle these challenges,we propose a traffic flow prediction model based on large language models(LLMs)to generate explainable traffic predictions,named xTP-LLM.By transferring multi-modal traffic data into natural language descriptions,xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data.The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data.Empirically,xTP-LLM shows competitive accuracy compared with deep learning baselines,while providing an intuitive and reliable explanation for predictions.This study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.
