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Real-Time Detection and Visualization of Traffic Conditions by Mining Twitter Data

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-15512-3_11

1611-3349

Sonia Khetarpaul, Dolly Sharma, Jackson I. Jose, Mohith Saragur,

Web Data Mining and Analysis

There have been various attempts to leverage the massive amount of data generated from social media websites. The real-time nature of social media platforms can help detect events, especially in a metropolitan city. In this paper, a system is proposed, that detects traffic-related events and road conditions in real-time from tweets by using classification algorithms and custom-trained named entity recognition model (NER) to classify and extract contextual information and visualise it on a map to get an overall picture of the traffic conditions in a city. The proposed system is versatile and can be applied to other use cases such as detecting calamities, social unrest, etc.