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

Wasen Melhem, Asad Abdi, Farid Meziane

2024

Abstract

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 research and real-world systems, and enabling advancements in intelligent transport systems (ITS).

Paper Citation

in Harvard Style

Melhem W., Abdi A. and Meziane F. (2024). Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 235-242. DOI: 10.5220/0013066900003838

in Bibtex Style

@conference{keod24,
author={Wasen Melhem and Asad Abdi and Farid Meziane},
title={Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={235-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013066900003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs
SN - 978-989-758-716-0
AU - Melhem W.
AU - Abdi A.
AU - Meziane F.
PY - 2024
SP - 235
EP - 242
DO - 10.5220/0013066900003838
PB - SciTePress