  Modern urbanization is demanding smarter technologies to improve a variety of
applications in intelligent transportation systems to relieve the increasing
amount of vehicular traffic congestion and incidents. Existing incident
detection techniques are limited to the use of sensors in the transportation
network and hang on human-inputs. Despite of its data abundance, social media
is not well-exploited in such context. In this paper, we develop an automated
traffic alert system based on Natural Language Processing (NLP) that filters
this flood of information and extract important traffic-related bullets. To
this end, we employ the fine-tuning Bidirectional Encoder Representations from
Transformers (BERT) language embedding model to filter the related traffic
information from social media. Then, we apply a question-answering model to
extract necessary information characterizing the report event such as its exact
location, occurrence time, and nature of the events. We demonstrate the adopted
NLP approaches outperform other existing approach and, after effectively
training them, we focus on real-world situation and show how the developed
approach can, in real-time, extract traffic-related information and
automatically convert them into alerts for navigation assistance applications
such as navigation apps.
