Explainable Traffic Flow Prediction with Large Language Models

arXiv:2404.02937v1 Announce Type: new
Abstract: Traffic flow prediction provides essential future views in the intelligent transportation system. Explainable predictions offer valuable insights into the factors influencing traffic patterns, which help urban planners, traffic engineers, and policymakers make informed decisions about infrastructure development, traffic management strategies, and public transportation planning. Despite their widespread popularity and commendable accuracy, prediction methods grounded in deep learning frequently disappoint in terms of transparency and interpretability. Recently, the availability of large-scale spatio-temporal data and the development …

abstract arxiv cs.ai cs.lg decisions development engineers flow future infrastructure insights intelligent intelligent transportation language language models large language large language models management patterns planning prediction predictions public public transportation strategies traffic traffic management transportation type urban