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

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.