【摘要】 交通事件检测是智能交通系统（ITS）的核心环节，现有方法在处理社交媒体非结构化文本、关联地理信息及协同多任务学习方面存在局限。为此，提出融合地理知识增强与多任务学习的交通事件检测（GeoKE-MTL）模型，以提升事件检测的准确性与鲁棒性，该模型主要由知识增强模块和多任务学习模块两个部分组成。实验结果表明，在自建的社交媒体文本数据集上，GeoKE-MTL在事发地点识别任务和交通事件识别任务中的F1分数分别达到79.42%和79.75%，在事发地点识别任务中均优于主流基线模型。研究验证了融合地理知识增强与多任务学习对提升检测性能的有效性，为智能交通系统的实时事件感知提供了新方案。更多还原

【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 results show that on a self-built social media text dataset, GeoKE-MTL achieves F1 scores of 79.42% and 79.75% in incident 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 enhancement with multi-task learning in improving detection performance, providing a new solution for real-time event perception in intelligent transportation systems.更多还原