智能科学与技术学报2025，Vol.7Issue(4)：493-504,12.DOI:10.11959/j.issn.2096-6652.202541
基于社交媒体数据的知识增强多任务学习交通事件检测模型
Knowledge-enhanced multi-task learning traffic incident detection model based on social media data
摘要
Abstract
Traffic incident detection is a core component of intelligent transportation systems(ITS),but existing methods are limited in processing unstructured social media text,associated geographic information,and collaborative multi-task learning.To address this,a traffic incident detection model based on integrated geographical knowledge enhancement and multi-task learning(GeoKE-MTL)was proposed to improve the accuracy and robustness of incident detection.The model consists of two main components:a knowledge enhancement module and a multi-task learning module.Experimental re-sults show that on a self-built social media text dataset,GeoKE-MTL achieves F1 scores of 79.42%and 79.75%in inci-dent location identification and traffic event identification tasks,respectively,outperforming mainstream baseline models in the incident location identification task.This study validates the effectiveness of integrating geographic knowledge en-hancement with multi-task learning in improving detection performance,providing a new solution for real-time event per-ception in intelligent transportation systems.关键词
交通事件检测/知识增强/多任务学习/智能交通系统Key words
traffic incident detection/knowledge enhancement/multi-task learning/intelligent transportation system分类
信息技术与安全科学引用本文复制引用
周正,汪玫,杨林瑶,李莉芳,王晓..基于社交媒体数据的知识增强多任务学习交通事件检测模型[J].智能科学与技术学报,2025,7(4):493-504,12.基金项目
国家自然科学基金项目(No.62173329) The National Natural Science Foundation of China(No.62173329) （No.62173329）
