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Modern urbanization is demanding smarter technologies to improve a variety ofapplications in intelligent transportation systems to relieve the increasingamount of vehicular traffic congestion and incidents. Existing incidentdetection techniques are limited to the use of sensors in the transportationnetwork and hang on human-inputs. Despite of its data abundance, social mediais not well-exploited in such context. In this paper, we develop an automatedtraffic alert system based on Natural Language Processing (NLP) that filtersthis flood of information and extract important traffic-related bullets. Tothis end, we employ the fine-tuning Bidirectional Encoder Representations fromTransformers (BERT) language embedding model to filter the related trafficinformation from social media. Then, we apply a question-answering model toextract necessary information characterizing the report event such as its exactlocation, occurrence time, and nature of the events. We demonstrate the adoptedNLP approaches outperform other existing approach and, after effectivelytraining them, we focus on real-world situation and show how the developedapproach can, in real-time, extract traffic-related information andautomatically convert them into alerts for navigation assistance applicationssuch as navigation apps.

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting