Detection and description of traffic events using floating car and social media data

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2022-9-12

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Ünsal, Ahmet Dündar

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Detection and verification of traffic events, in traffic management, can be performed traditionally using roadside sensor data. More recently wide coverage travel time information obtained from floating car data (FCD) is also used, despite its limitations to describe the event characteristics and requires verification. Social media, widely adopted in our daily lives, hosts a sheer amount of data which can be analyzed to identify incidents and events using information retrieval methods. In this study, a framework is proposed to detect and describe traffic events in real-time using two independent data sources, FCD and Social Media Data (SMD). Traffic event related tweets in SMD are classified using a language model which is tailored to handle agglutinative nature of Turkish language. Detected traffic event tweets are geolocated using a custom named-entity recognition (NER) integrated, knowledge-based geocoding approach, which achieves a median positional error of 379.2 meters. In FCD, proposed detection tasks identified non-recurrent congestions (NRCs) with their spatiotemporal impact areas. Matching experiments using spatiotemporal information showed that 64.1% of traffic event reporting tweets can be verified by an NRC, whereas only 33% of the large-scale NRCs are verified by a tweet.

Subject Keywords

Traffic Event Detection, Intelligent Transportation Systems, Floating Car Data, Social Media

URI

https://hdl.handle.net/11511/99672

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

A. D. Ünsal, “Detection and description of traffic events using floating car and social media data,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.