Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2020 IEEE Technology and Engineering Management Conference, TEMSCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728142241
StatePublished - Jun 1 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13


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