TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

arXiv:2403.02221v1 Announce Type: new
Abstract: Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management. The precision of prevailing deep learning-driven traffic prediction models typically sees an upward trend with a rise in the volume of training data. However, the procurement of comprehensive spatiotemporal datasets for traffic is often fraught with challenges, primarily stemming from the substantial costs associated with data collection …

abstract arxiv cs.lg deep learning facet framework intelligent intelligent transportation language language models large language large language models management pivotal precision prediction prediction models predictions significance systems traffic traffic management transportation trend type