Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs

Traffic data analysis and forecasting is a multidimensional challenge that extracts details from sources such as social media and vehicle sensor data. This study proposes a three-stage framework using Deep Learning (DL) and natural language processing (NLP) techniques to enhance the end-to-end pipeline for traffic event identification and forecasting. The framework first identifies relevant traffic data from social media using NLP, context, and word-level embeddings. The second phase extracts events and locations to dynamically construct a knowledge graph using deep learning and slot filling. A domain-specific large language model (LLM), enriched with this graph, improves traffic information relevancy. The final phase integrates Allen's interval algebra and region connection calculus to forecast traffic events based on temporal and spatial logic. This framework’s goal is to improve the accuracy and semantic quality of traffic event detection, bridging the gap between academic researc h and real-world systems, and enabling advancements in intelligent transport systems (ITS)