Traffic event detection as a slot filling problem

Social media platforms, such as Twitter, can be used to extract information related to traffic events. Previous works focused mainly on classifying tweets into predefined categories (i.e., traffic or non-traffic) without many details of traffic events. However, extracting traffic-related fine-grained information from tweets is essential to build an intelligent transportation system. In this work, we address for the first time the problem of detecting traffic events using Twitter as two subtasks: (i) identifying whether a tweet is traffic-related or not as a text classification subtask, and (ii) extracting more fine-grained information (i.e., what, when, where, and the consequenceof the traffic event) as a slot filling subtask. We also publish two Dutch Traffic Twitter datasets from Belgium and the Brussels capital region. We propose using deep learning based methods that process the two subtasks separately or jointly. Experimental results indicate that the proposed architectures achieve high performance scores (i.e., more than 95% F1 score) on the constructed datasets for both subtasks, even in a transfer learning scenario. In addition, incorporating tweet-level information in each of the tokens comprising the tweet (for the BERT-based model) can lead to a performance improvement for the joint setting. Our datasets and code are available on GitHub.1

Automated summary

This study addresses the problem of detecting traffic events using Twitter and proposes two subtasks: a text classification subtask to identify whether a tweet is traffic-related or not, and a slot filling subtask to extract fine-grained information about the traffic event. Experimental results show that the proposed deep learning methods achieve high performance scores (95%+ F1 score) on the constructed datasets for both subtasks, even in a transfer learning scenario. The Dutch Traffic Twitter datasets from Belgium and the Brussels capital region, as well as the code, are available on GitHub.