  In this paper, we introduce the new problem of extracting fine-grained
traffic information from Twitter streams by also making publicly available the
two (constructed) traffic-related datasets from Belgium and the Brussels
capital region. In particular, we experiment with several models to identify
(i) whether a tweet is traffic-related or not, and (ii) in the case that the
tweet is traffic-related to identify more fine-grained information regarding
the event (e.g., the type of the event, where the event happened). To do so, we
frame (i) the problem of identifying whether a tweet is a traffic-related event
or not as a text classification subtask, and (ii) the problem of identifying
more fine-grained traffic-related information as a slot filling subtask, where
fine-grained information (e.g., where an event has happened) is represented as
a slot/entity of a particular type. We propose the use of several methods that
process the two subtasks either separately or in a joint setting, and we
evaluate the effectiveness of the proposed methods for solving the traffic
event detection problem. Experimental results indicate that the proposed
architectures achieve high performance scores (i.e., more than 95% in terms of
F$_{1}$ score) on the constructed datasets for both of the subtasks (i.e., text
classification and slot filling) even in a transfer learning scenario. In
addition, by 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.
