Knowledge-enhanced multi-task learning traffic incident detection model based on social media data

ZHOU Zheng; WANG Mei; YANG Linyao; LI Lifang; WANG Xiao

doi:10.11959/j.issn.2096-6652.202541

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Knowledge-enhanced multi-task learning traffic incident detection model based on social media data

Topics\：Social Computing and Resilient Society | 更新时间：2026-03-18

- Knowledge-enhanced multi-task learning traffic incident detection model based on social media data
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 4, Pages: 493-504(2025)
- 作者机构：
  
  1.安徽大学人工智能学院，安徽合肥 230031
  2.之江实验室，浙江杭州 311121
  3.中山大学新闻传播学院，广东广州 510006
  4.光电信息获取与防护技术全国重点实验室，安徽合肥 230031
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62173329)
- DOI：10.11959/j.issn.2096-6652.202541
  CLC： TP39
- Received：18 September 2025，
  
  Revised：2025-11-25，
  
  Accepted：26 November 2025，
  
  Published：15 December 2025
- 稿件说明：
移动端阅览
周正,汪玫,杨林瑶等.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,2025,07(04):493-504.

ZHOU Zheng,WANG Mei,YANG Linyao,et al.Knowledge-enhanced multi-task learning traffic incident detection model based on social media data[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):493-504.
周正,汪玫,杨林瑶等.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,2025,07(04):493-504. DOI： 10.11959/j.issn.2096-6652.202541.

ZHOU Zheng,WANG Mei,YANG Linyao,et al.Knowledge-enhanced multi-task learning traffic incident detection model based on social media data[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):493-504. DOI： 10.11959/j.issn.2096-6652.202541.

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。

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.

关键词

Keywords

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