MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter

He, Jiaji; Wu, Shi Hong; Alhamadani, Abdulaziz; Chen, C.; Lu, W.W.; Lu, Chang-Tien; Solnick, David; Li, Yanlin

doi:10.1145/3539618.3591807

Cited by 2 publications

(1 citation statement)

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“…NB, RF, SVC, linear SVC, BiLSTM, and TextCNN [38] Traffic jams, smooth traffic CNN+Word2Vec, CNN+FastText, and SVM [39] Education, transportation, environment, geospatial and water event DNN and HHO [42] Congestion, accidents, social gatherings, thefts, bus queues, floods, and electricity charges. AWD-LSTM and ULMFiT [43] Incident and non-incident BERT www.ijacsa.thesai.org 5) Other Methods: He et al [44] proposed MetroScope system which analyzed real-time tweets to detect events related to the Washington D.C. Metro system. They developed a phrase-level algorithm that groups events with similar key phrases into a story.…”

Section: A Event Detection Techniquesmentioning

confidence: 99%

Smart Cities, Smarter Roads: A Review of Leveraging Cutting-Edge Technologies for Intelligent Event Detection from Social Media

Alomari,

Mehmood

2023

IJACSA

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The rapidly evolving landscape of smart cities and intelligent transportation systems makes the timely detection of traffic events a critical element for optimizing urban mobility. Furthermore, social media emerges as a valuable source of realtime information, with users acting as active sensors who spontaneously share observations and experiences related to traffic incidents. This review paper offers a comprehensive understanding of the state-of-the-art in traffic event detection from social media. The paper explores leveraging cutting-edge technologies including machine learning, and deep learning with big data technologies and high-performance computing. The discussion unfolds with an in-depth examination of the recent approaches for event detection followed by an exploration of the techniques of spatio-temporal information extraction and sentiment analysis, which are both considered fundamental aspects in enhancing the contextual understanding of traffic events. Further, the review explores the pivotal role of big data technologies in addressing scalability challenges inherent in the vast expanse of social data. The examination encompasses how big data frameworks facilitate efficient storage, processing, and analysis of large-scale social media datasets, thereby empowering machine learning and deep learning models for robust and realtime traffic event detection. Subsequently, the challenges and future directions have been highlighted. Addressing these challenges and leveraging advanced technologies, facilitates the proactive detection and management of these events, paving the way for smart mobility systems.

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